Artificial Intelligence
SWE-MILE: Asynchronous Potential-Induced Milestone Credit Assignment for Long-Horizon Software Engineering Agents
Chaoqun Cui, Hao Zhou, Meiqi Chen et al. (+2 more)
Long-horizon software engineering (SWE)
agents trained with reinforcement learning with verifiable rewards (RLVR) typically receive only terminal outcome supervision, making it difficult to distinguish productive actions from redundant exploration or functional regressions. We propose SWE-MILE, an asynchronous potential-induced milestone credit assignment framework that derives fine-grained process supervision from workflow runtime, without auxiliary reward models or external evaluators. SWE-MILE quantifies task-relevant file exposure and test-state
alignment as
navigation and verification potentials, respectively. Differences in these potentials attribute milestone progress and regressions to individual actions, while discounted backward credit propagates supervision to preceding steps. To efficiently acquire intermediate verification states, SWE-MILE further introduces asynchronous shadow probing, which replays repository-changing actions in an isolated sandbox and runs verification in parallel with the
agent's primary interaction, largely hiding verification latency. The resulting process credit augments terminal outcome advantages and provides informative learning signals. Experiments on two representative long-horizon SWE tasks demonstrate substantial improvements in
agent performance, highlighting workflow runtime signals as a practical source of process supervision for long-horizon SWE
agents.
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All authors Chaoqun Cui, Hao Zhou, Meiqi Chen, Fandong Meng, Wenji Mao
Computation and Language
ExpVoyager: Direct Experience Navigation for Dynamic Agent Skill Synthesis
Kwangwook Seo and Dongha Lee
Learning from experience in LLM
agents has become a key paradigm for developing self-evolving
agents that continuously learn and expand their capabilities. Within this paradigm, synthesizing the
agent skill has emerged as a promising solution for transforming accumulated experience into reusable procedural knowledge, serving as an important layer for the harness system that supplies
agents at runtime. Despite its potential, existing approaches largely abstract past experience into fixed procedural knowledge before downstream demands are known, which risks discarding knowledge that later becomes critical while retaining instance-specific details irrelevant to future tasks. In this paper, we reframe
agent skill synthesis as a dynamic
navigation problem over past experience, where
agents actively explore accumulated trajectories on demand for the current task with targeted and fine-grained access to experience knowledge. To this end, we propose ExpVoyager, a novel framework in which a skill curator navigates raw experience across different views and resolutions, continually identifying reusable procedural knowledge from what it observes while tracking remaining knowledge needs that guide where to navigate next. Extensive experiments demonstrate both the effectiveness and versatility of ExpVoyager, showing consistent improvements in downstream task performance, continual gains as the experience space scales, and practical compatibility with existing skills under efficient experience access.
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Computer Vision
DraftAttention2: Fast Video Diffusion with Low-Resolution-Guided Mixed-Precision Attention
Rui Ding, Haopeng Li, Weize Ma et al. (+10 more)
Video generation has broad applications in content creation and entertainment.
Diffusion transformers have advanced the quality of generated
videos, but
attention over spatiotemporal
tokens becomes increasingly expensive as
video resolution and duration increase. We present DraftAttention2, a training-free framework that uses the low-resolution draft
attention map to jointly select
attention blocks and assign their numerical precision. Specifically, spatial 2D average- and max-pooled queries and keys capture complementary regional statistics to estimate block importance, and a shared ranking assigns higher precision to important blocks, lower precision to less important retained blocks, and skips the rest under configurable budgets. Our analysis separates sparsification error from
attention-weighted quantization error, establishing when recovering skipped interactions with low-bit computation tightens the output-error bound. This analysis motivates retaining more interactions at low precision while reserving higher precision for blocks with larger
attention mass. To translate these fine-grained assignments into practical speedups, we further develop fused operand preparation and a single
attention kernel with precision-specific phases, sharing data movement, softmax statistics, and output accumulation across precisions. Experiments demonstrate that our method achieves a superior quality-efficiency trade-off over existing efficient
video generation methods. Notably, its advantage is particularly pronounced for few-step
video diffusion, where jointly combining sparsity with 4- and 8-bit mixed-precision computation substantially improves generation quality while retaining significant acceleration. Code is available at https://github.com/anemoi-project/anemoi
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All authors Rui Ding, Haopeng Li, Weize Ma, Yufa Zhou, Yitong Li, Xiaoling Zhou, Jiashuo Cao, Liyang Li, Hua Geng, Jiuxiang Gu, Jun Lin, Enze Xie, Xuan Shen
Emerging Technologies · Cross-listed from quant-ph
Fewer Qubits, Better Choices: Coupling-Aware Sub-QUBO Selection for Quantum-Assisted Traffic Zone Partitioning
Qianwen Guo and Ruimin Ke
Near-term
quantum optimizers accommodate roughly a hundred binary variables, requiring large Quadratic Unconstrained Binary Optimization problems to be decomposed into hardware-sized subproblems while other variables remain fixed. Variables are typically selected by ranking the objective changes from individual flips. We show that this rule is uninformative once the incumbent is optimal under single-variable flips: every score represents a cost, while remaining improvements depend on overlooked pairwise interactions. A second-order expansion instead yields a prize-collecting densest-subgraph selection problem, solvable greedily in time linear in the number of variables, and two solver-free bounds bracketing the improvement achievable within a selected subset. On transportation networks from Chicago and Philadelphia, our rule with 16-variable subproblems outperforms random selection with 64, demonstrating that quadrupling device capacity cannot compensate for weaker selection. Replacing
geometric adjacency with road-network connectivity widens the margin by a factor of 1.6-1.9. With the selection trace fixed, varying the fraction of subproblems solved on a 120-
qubit device from zero to one leaves the final objective unchanged to six significant figures. At tested hardware-accessible sizes, gains therefore arise from selection rather than the backend. Hardware feasibility is limited by coupling terms: 120-variable problems fail to compile with 7,260 terms but succeed with 5,118. Compilation scales almost quadratically with term count and dominates execution cost, reaching 1,752 seconds versus 469 seconds of
quantum processing.
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Robotics
World SLAM Model: Joint World Modeling for SLAM and Navigation
Minghui Qin, Yijun Yuan, Weicheng Zheng et al. (+7 more)
We introduce World SLAM Model (WSM), a unified framework that brings the SLAM paradigm directly into downstream
navigation. Rather than treating SLAM merely as an upstream module that provides poses, maps or
tokens, WSM adopts its core mechanisms, including incremental state updates with persistent
memory and backend refinement of accumulated errors, to maintain a consistent world state during interaction. Given the current observation and a
navigation goal, WSM predicts future visual states and jointly estimates their camera motion and dense geometry, grounding visual prediction in an evolving spatial world state. This spatial state is continuously updated as new observations arrive and provides the basis for action generation and closed-loop
navigation. WSM is trained end-to-end with a joint
navigation–SLAM objective, enabling downstream
navigation to benefit directly from SLAM-style state maintenance and refinement while preserving accurate
geometric estimation. Experiments demonstrate improved
navigation performance together with strong SLAM accuracy, highlighting the potential of SLAM as an intrinsic mechanism for long-horizon world modeling and
embodied interaction.
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All authors Minghui Qin, Yijun Yuan, Weicheng Zheng, Kenan Li, Weibang Wang, Chang Sun, Junhao Huang, Anmin Liu, Yicheng Yao, Hang Zhao
Computation and Language
MixDetect: Word-Level Localization and Quantification of AI Editing
Hongrui Bao, Yubing Ren, Zhendong Pan et al. (+3 more)
Large
language models are increasingly used to edit human-written text rather than generate entire texts from scratch. Conventional AI-text detectors mainly distinguish human-written from fully AI-generated text, while recent methods for AI-edited text typically provide only a text-level label or editing-degree score. We introduce MixDetect, a word-level framework for localizing and quantifying AI editing. MixDetect separately predicts whether each word has been edited and, conditional on editing, how substantial the edit is, allowing editing scope and editing intensity to be estimated separately. During training, source–edited pairs are aligned to construct word-level supervision, while inference requires only the input text. Experiments show that MixDetect accurately localizes AI-edited words, reflects differences in editing intensity, and reveals different scope–intensity patterns across editing degrees and operations. The overall AI editing magnitude increases under additional AI editing, decreases when AI-generated text is edited by humans, and remains nearly unchanged under ordinary human-to-human editing. The aggregated text-level predictions also perform well on binary and ternary AI-text classification and remain effective under domain and generator shifts. These results show that AI editing can be analyzed beyond a single authorship label or editing-degree score by identifying both where AI editing occurs and how substantial the edits are.
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All authors Hongrui Bao, Yubing Ren, Zhendong Pan, Fang Fang, Shi Wang, Yanan Cao
Computation and Language
Does CoT-Pass@k Really Check the CoT? A Multilingual Mathematical Audit
Tar{\i}k Tuna Taşalt{\i}, Burcu Hüdaverdi and David Semedo
Pass@k measures whether a model reaches a correct answer under repeated sampling, but never how: a lucky guess counts the same as sound
reasoning. CoT-Pass@k was proposed to close that gap, adding an LLM-as-judge that must assess a solution's
reasoning chain before it counts. Its value rests entirely on one assumption: that the judge catches flawed
reasoning. That assumption has never been tested inside the metric that depends on it, and never outside English, though the metric's claims concern models used in many
languages. We report the first audit of that verification step, run under the metric's own protocol on a
multilingual suite of five mathematical
benchmarks in English, Turkish and Portuguese, two of them natively written. We corrupt correct solutions with deterministic edits that damage the chain and the final answer separately. We observe that all three judges accept corrupted chains almost as often as clean ones. V4-Flash and Qwen3.6 reject a solution sharply only when its final answer is wrong and accept a wrong answer more readily when the chain agrees with it; the metric's own judge accepts most wrong answers as well. Our study shows that chain-answer agreement dominates the two larger judges' verdicts and that all three fail to reliably detect the tested
reasoning errors. Consequently the difference Pass@k - CoT-Pass@k averages 19.7 points on an earlier solver generation but only 4.1 on the current one. What little remains depends on the
token budgets on both sides and on the generation mode; raising the generation budget moves Pass@64 by more than fifty points while the difference stays at zero. We close with two checks any judged
reasoning metric should pass before its numbers are read as evidence about
reasoning.
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Machine Learning
Intuition vectors
Shahar Haim and Daniel C. McNamee
Large self-supervised
vision models learn representations that support scene
segmentation and the semantic decomposition of physical objects. We ask whether their representational geometry supports transfer to visual
reasoning problems without any task-specific fine-tuning. We hypothesized that relational representations may bridge perception and abstract
reasoning by encoding similarities and transformations among visual inputs such that an intuitive, implicit form of
reasoning may be performed via latent vector arithmetic. Specifically, we examine DINOv3, MAE, and random pixel projections on abstract and naturalistic Bongard problems, ARC-AGI-1 and ARC-AGI-2, and novel ARC-GEN instances. On both Bongard
benchmarks, the accuracy of a simple nearest-centroid readout of frozen visual
embeddings is within four percentage points of the task-specific baselines reported with the original
benchmarks. In ARC, latent difference vectors summarizing demonstration input-output transformations, which we refer to as intuition vectors, show greater
alignment with test vectors from the same task, whereas those from unrelated tasks are near orthogonal. This latent geometry is operational: transporting a query along its intuition vector consistently improves exact-output
retrieval, reaching 70.7 on ARC-AGI-2 evaluation. Across 397,000 ARC-GEN instances from 794 tasks, single-pair intuition vectors identify the generating task with approximately 87% leave-one-out accuracy. These findings suggest that latent vector arithmetic over frozen visual representations supports implicit rule inference across varied problem domains without a generative model component, indicating that inferring an abstract transformation and generating its instance-specific consequence may be separable capacities.
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Software Engineering
Evolution of Deprecated APIs and Their Replacements in Python Libraries
Gangqiang He and Guanping Xiao
Context: API deprecation is common in library
evolution, but migration requires finding replacements and adapting calls. Prior studies examine deprecation, replacement identification, and client migration separately, leaving deprecated API-replacement API relationships across releases and lifecycle states insufficiently understood. Objective: We examine replacement locality, parameter-interface differences, changes in replacement rankings across versions, and post-deprecation lifecycles from source-definition and original-invocation perspectives. Method: We construct 830 maintainer-specified mappings from 33 Python libraries, covering classes, functions, and methods. We compare definitions, track similarity-based rankings under two migration scenarios, and identify lifecycle events through source inspection and release-by-release execution. Results: Same-module replacements account for 39.1%, 55.6%, and 79.9% of class, function, and method mappings, respectively. Most mappings have no parameter changes, but function mappings show more frequent and diverse changes. Rankings vary across releases and code representations, and the applicable methods agree on the overall trend in only about 60% of cases. Other-candidate effects are dominant or mixed in 49.0% of key versions. After deprecation, 56.4% of mappings retain the source definition and 68.6% retain an executable original invocation. Among 753 mappings with at least one observed ending event, 25.2% are source-first or invocation-first rather than same-release. Of these cases, 77.4% are source-first. Conclusions: Deprecated API-replacement API relationships depend on API granularity, release context, code representation, candidate competition, and lifecycle state. These results support
evolution-aware replacement API recommendation and automated API migration.
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Computation and Language
The Alignment Paradox: How Post-Training Amplifies Confident Hallucinations in Language Models
Qingjia Huang, Yakai Li, Jianguo Wu et al. (+5 more)
Large
language models (LLMs) can produce factually incorrect answers with high confidence, undermining their reliability and limiting the effectiveness of uncertainty-based error detection. While prior research attributes confident
hallucinations to factors such as missing knowledge in training data,
reasoning errors, or
stochastic decoding, we uncover that post-training
alignment itself is a primary driver of these errors, a phenomenon we call the
Alignment Paradox. Across five model families evaluated on factual
benchmarks, unaligned base models produce few high-confidence errors on long-tail factual queries, whereas instruction-tuned models multiply high-confidence errors ($p \ge 0.95$) by more than an order of magnitude (10$\times$ to 35$\times$). Layer-wise probing with the Logit Lens reveals that this overconfidence emerges in late layers, where wrong-answer margins expand past 4.0 points after remaining near zero across early and intermediate layers. These findings motivate limiting margin growth during post-training. We implement this principle through an
entropy-dependent margin bound in direct preference optimization (DPO). In multi-epoch experiments with Mistral-7B, the bounded objective reduces high-confidence errors by up to 35.3% relative to standard DPO while maintaining performance on evaluated general
reasoning benchmarks. These results show that bounded margins mitigate confident
hallucinations during post-training.
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All authors Qingjia Huang, Yakai Li, Jianguo Wu, Qihang Zhou, Aimin Yu, Xiaoqi Jia, Luping Ma, Weijuan Zhang
Artificial Intelligence
"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused
Xutao Mao, Rui Qian, Longxiang Wang et al. (+6 more)
LLM
agents increasingly keep working after a task succeeds as they resume after compaction or take over handoffs. Their finished work keeps receiving follow-up input that sometimes falsely accuses it for later failures. We call an
agent's acceptance of such a false accusation gaslight sycophancy, and destructive over-correction when acting on it damages previously correct work. We introduce CAVE-Bench, a
benchmark of 365 agentic tasks across six domains built around opaque tasks. Every scored run first reaches a verified correct state, whose supporting rationale and history stay in the workspace while the facts that would settle the accusation lie in external or runtime state beyond the
agent's reach. The
agent cannot confirm or refute the claim with a local check, so the right response should keep the work and ask for the missing evidence. Each task either hands the
agent correct work with saved evidence or let it build and verify that work first, and five risk factors set how the accusation enters the workflow. We score accusation acceptance and evidence use from the trajectory and measure harm by deterministic replay of downstream events. Across 14 of the latest models in Claude Code, false accusations damage correct work in up to 60.06% of runs, and stronger models often do so after recovering the supporting evidence. The same model behaves differently across OpenCode, Codex, and Hermes, and a harness gate driven by the
benchmark's live signals cuts replayed harm by 74%. These results show that preserving already-correct work under unsupported accusation is a distinct safety challenge for long-lived
agents. Our project is in https://henrymao2004.github.io/
agent-over-correction/.
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All authors Xutao Mao, Rui Qian, Longxiang Wang, Xinjian Yi, Mingxuan Li, Linghan Chen, Yudong Gao, Xiang Zheng, Cong Wang
Machine Learning
Stabilizing the Dynamic Low-Rank Training
Zhonghan Xu, Ling Wang, Junhao Chen et al. (+2 more)
Training neural networks directly in a low-rank parameterization is an appealing route to reducing
memory, compute, and storage simultaneously during both training and inference. Dynamic low-rank training (DLRT), which confines weights to a rank-$r$
manifold via the Galerkin projection of the gradient flow, is particularly attractive because it identifies efficient subnetworks on the fly without specialized initialization or post-factorization. However, DLRT fails to find trainable networks under high compression. In this paper, we derive the gradient flow of the best rank-$r$ approximation and point out that the offset of DLRT comes from a
curvature-coupling term which is large and thus non-negligible under aggressive compression. Guided by this analysis, we propose a stable dynamic low-rank training method, named SDLRT, which maintains a lightweight compensation buffer that reinjects the top neglected singular directions. Additionally, we introduce a negative feedback on the truncation tolerance to stabilize each layer's rank. Experimentally, SDLRT reliably finds trainable subnetworks where DLRT collapses and as a PEFT adapter on DeBERTa-v3, it achieves the best average score on SuperGLUE at only $2.8\%$ parameter overhead over LoRA.
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All authors Zhonghan Xu, Ling Wang, Junhao Chen, Jianwei Zhao, Jinwei Yang
Machine Learning
Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification
Harshit Kumar, Sujan Chakraborty, Priyanka Saha et al. (+2 more)
Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popular remedy, but rewiring methods are usually evaluated with a single classifier, which makes it hard to tell whether the gains come from the new
topology or from that particular pairing. We propose an affinity-guided rewiring method that estimates the graph and the node representation together. It alternates, in the spirit of expectation maximisation, between training a lightweight graph neural network on the current graph and re-weighting candidate edges under a modularity objective with a pseudo-label homophily term. Candidate edges come from a compact pool scored by a contrastively learned node similarity and a neighbourhood-distribution affinity. The method returns two classifier-independent outputs: a rewired graph and a node
embedding learned on it. Across six heterophilic
benchmarks and five downstream classifiers, it improves accuracy over the original graph with normalised features in 23 of 30 classifier-
dataset combinations, with a mean gain of 5.8 points, and reduces the accuracy spread between classifiers about fourfold. A controlled ablation shows that the two outputs are each useful and play complementary roles: the
embedding contributes most of the accuracy gain, while the rewired graph makes different classifiers agree. A fully unsupervised variant, which uses no labels during rewiring, retains most of the improvement. The rewired graphs are also more homophilic and improve label propagation and community detection.
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All authors Harshit Kumar, Sujan Chakraborty, Priyanka Saha, Pritam Kar, Saptarshi Bej
Computer Vision
Levy-Driven Correspondence Estimation for Registration
Qianliang Wu, Jiaqi Yang, Wankou Yang et al. (+4 more)
Finding reliable point correspondences is difficult when point clouds have low overlap or undergo non-rigid deformation. Iterative refinement can correct uncertain matches, but costly network evaluations limit the number of updates. We present LevyMatch, a Lévy-driven method that uses random jumps to refine a soft matching
matrix. At each step, a network uses the current matching state and
geometric information to predict a target matching
matrix. A Brownian reference bridge gives an explicit formula for the update toward this target. A Gamma random clock sets the time step for each update. The updated matches provide new
geometric feedback for the next target prediction. We further propose a fixed front-loaded Gamma policy that assigns more expected clock time to early updates and less to later ones, without retraining or extra network evaluations. Reordering the same sampled Gamma increments shows that placing larger increments early gives higher accuracy than placing them late. On 4DMatch and 4DLoMatch, our method improves both non-rigid feature matching recall (NFMR) and inlier ratio (IR) over the compared methods. The front-loaded policy achieves 93.09% NFMR and 92.11% IR on 4DMatch, and 82.79% NFMR and 79.07% IR on 4DLoMatch.
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All authors Qianliang Wu, Jiaqi Yang, Wankou Yang, Le Hui, Jin Xie, Jian Yang, Yaqing Ding
Computation and Language
KV-Lingo: Learning KV-Cache Translators with Distillation
Valérie Castin, Keitaro Sakamoto, Anastasiia Filippova et al. (+3 more)
Large
language models represent context with a key-value (KV) cache. Caches are model-specific: for the same text, models with different architectures or weights produce incompatible representations. This makes it costly to switch models over a shared context: although the context has already been processed by one model, the incoming model must process it again to build its own cache. We introduce KV-Lingo, a method for translating the KV cache of a source model into one that can be read by a target model. KV-Lingo consists of a collection of linear maps, typically one per layer of the target model, that are applied independently on all
tokens' key and value representations. We train these maps using
distillation, minimising the divergence between the target model's predictions from its native cache and those from the translated cache. We consider several model pairs spanning multiple sizes and architectures, training one translator per pair on a generic text corpus. The resulting translators preserve strong downstream performance in both small-to-large and large-to-small transfers. Since a switch then costs a linear map and a single decoding step instead of a prefill, replacing re-prefill with cache translation reduces the time to first
token after a model switch by 9.6x already on a 64-
token prompt for Qwen models on an Apple M3 Ultra, and by up to 29x at 32k context length on an H100. These gains make KV-Lingo particularly useful for dynamic model routing: a context can be processed by one model and handed off to another only when needed, without re-prefilling the shared prefix. We finally show that KV-Lingo can be used for seamless model switching, staying close to re-prefill across repeated switches in our multi-turn evaluations.
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All authors Valérie Castin, Keitaro Sakamoto, Anastasiia Filippova, João Monteiro, Marco Cuturi, Pierre Ablin
Sound · Cross-listed from eess.AS
Yang Xiao, Vidhyasaharan Sethu, Eun-Jung Holden et al. (+1 more)
Spoken conversational systems must recover information from prior interactions (i.e.,
memory), yet relevant information in
speech extends beyond what was said to who said it, how it was spoken, and what was audible, information that exists only in the audio signal and cannot be recovered from a transcript. Beyond what to remember,
memory also demands diverse operations: retrieving a single fact, integrating evidence across turns, tracking an evolving state. Real interactions further unfold across sessions, meaning information accumulates across distinct episodes rather than a single continuous recording. Existing
benchmarks fall short on all three dimensions: they focus primarily on lexical content, adopt limited and ad hoc
memory operations, and treat
memory as a single-session problem. We argue that principled
memory evaluation requires jointly characterizing the acoustic evidence to be retained and the operations applied to it, and introduce a taxonomy along these two axes. Building on this taxonomy, we present VoxMem: 3,196 evaluation instances over 34,743 spoken sessions (177 hours) crossing four acoustic evidence types (
speech semantics, speaker identity, paralinguistic cues, environmental sound) with four
memory operations (information extraction, multi-session
reasoning, temporal tracking, and answer refusal), grounded in multi-session histories and stratified across context budgets from 8K to 64K
tokens. Evaluating 15 LALMs, no model exceeds 40% at 32K. Models retain what was said far better than who said it, how, or what was audible, a gap that widens for complex operations, grows with history length, and manifests as qualitatively distinct failure modes across evidence types. VoxMem aims to provide a foundation to measure and drive progress on the full scope of spoken conversational
memory.
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All authors Yang Xiao, Vidhyasaharan Sethu, Eun-Jung Holden, Ting Dang
Data Structures and Algorithms
Differentially Private Approximation of the John Ellipsoid
Bar Mahpud, Daniel Omer and Or Sheffet
We study the problem of approximating the John ellipsoid (JE) of a given (centrally symmetric) polytope of $n$ constraints in a Euclidean space under differential
privacy (DP). We give the first differentially private
algorithm for this problem under the standard model, where neighboring
datasets may differ arbitrarily in one a single constraint. Our work also extends to the complimentary problem of Minimum Enclosing Ellipsoid of $n$ points in the Euclidean space. Our approach is based on the recent non-private multiplicative-weights
algorithm of~\cite{pmlr-v99-cohen19a}. First we introduce a non-private generalization of the Cohen et al
algorithm, yielding a $(1+\gamma)$-approximation of the JE problem while violating at most $\kappa n$ constraints in $O(\log(1/\kappa)/\gamma)$ iterations. This variant works by projecting the intermediate weights assigned to the constraints onto the set of $\kappa$-dense distributions, similarly to~\cite{bun2020efficientnoisetolerantprivatelearning}. We then design a $\rho$-zCDP variant of this
algorithm by adding Gaussian noise to the weighted covariance
matrix aggregated in each step of the
algorithm. Under a mild goodness assumption on the data we can assert that the resulting noisy
matrix is close to the true
matrix, thereby achieving essentially the same guarantee as the non-private
algorithm provided sufficiently many input points. Thus our method achieves an efficient DP poly-time
algorithm under concrete sample complexity bounds.
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Cryptography and Security · Cross-listed from cond-mat.dis-nn
Dense auto-hetero associative memories applied to noisy communication channels
Elena Agliari, Andrea Alessandrelli, Adriano Barra et al. (+1 more)
Networks of interacting Hebbian networks have recently been shown to perform a task beyond associative
memory, namely pattern disentanglement: when fed with a spurious mixture of stored patterns, the different modules spontaneously specialize on, and retrieve, the different constituents of the mixture. So far, this capability has only been established for pairwise interactions, which limits the number of patterns that can be handled. Here we introduce a dense extension of these modular networks, in which both the auto-associative couplings within each module and the hetero-associative couplings among modules are promoted to higher-order Hebbian interactions. We show that, with a suitable choice of the interaction orders, the network disentangles mixtures while storing a number of patterns that scales linearly with the module size, a regime where its pairwise counterpart fails. Through a statistical-mechanical analysis based on Guerra's interpolation, we derive the self-consistency equations for the order parameters and draw the phase diagrams identifying the region where disentanglement is achieved; these predictions are confirmed by Monte Carlo simulations. Finally, we show that disentanglement provides a natural decoding primitive, and we illustrate it with two applications: the explicit reconstruction of all the hidden patterns from the Hebbian tensors and a stream of unlabeled mixtures, and a proof-of-concept communication protocol in which each message
token is transmitted as a masked mixture of hidden patterns and decoded by the network dynamics. Owing to its attractor-based decoding, the protocol degrades gracefully under strong channel corruption, where conventional secure-transmission pipelines fail abruptly.
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All authors Elena Agliari, Andrea Alessandrelli, Adriano Barra, Alberto Fachechi
Human-Computer Interaction
When the Environment Becomes the Interface: Multisensory Environmental Interfaces for Human-AI Interaction in Autonomous Vehicles
Keqi Chen, Runjia Tan, Xinyi Fu et al. (+3 more)
As AI increasingly assumes operational control, human-computer interaction is shifting from operating systems through explicit interfaces to inhabiting intelligent environments. This raises a fundamental question: when users no longer directly manipulate a system, what mediates their relationship with intelligent technologies? We introduce environmental interfaces: designed environmental conditions that shape human-AI relationships through ambient, holistic, and evaluative pathways rather than explicit functional interaction. Using autonomous vehicle cabins as a revealing context, we conducted a within-subject experiment with 24 participants (216 observations), manipulating lighting and scent in a simulated autonomous driving environment. Three findings emerged. First, perceived atmosphere accounted for 66.5% of the variance in overall journey experience, showing that environmental conditions can function as an interface rather than merely a supporting design element. Second, multisensory processing followed a hierarchical architecture: individual sensory appraisals were initially independent, while cross-modal integration emerged during higher-order environmental evaluation. Third, olfactory stimuli influenced affective responses and experience evaluation more strongly than visual stimuli, challenging the visual dominance of automotive interaction design. Environmental quality and sensory congruency also predicted trust in the autonomous system, suggesting that passengers may use environmental cues as proxy signals when direct assessment of AI competence is difficult. These findings establish environmental interfaces as a distinct interaction modality and point to a broader transition from designing interfaces for operating intelligent systems to designing environments for inhabiting them.
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All authors Keqi Chen, Runjia Tan, Xinyi Fu, Shanhe Lou, Kwan Min Lee, Chen Lv
Cryptography and Security · Cross-listed from quant-ph
Beyond NISQ Assumptions: One-time Memory in the Classically Accessible Random-Oracle Model
Boyang Chen, Tianren Liu and Luojian Wei
Quantum information enables many cryptographic primitives that are impossible in the classical world. A line of works has developed cryptographic protocol under the assumption that
quantum adversaries are restricted to noisy intermediate-scale
quantum (NISQ) computing power, enabling strong one-time functionalities. But the advent of early fault-tolerant
quantum computers eras will allow deeper logical
quantum circuits, calling into questions the applicability of these NISQ-based assumptions. In this work, we adapt the classically accessible random oracle model (CAROM) as in [BDF+11] and [AK22], in which adversaries are only allowed to classically query the random oracle. The restriction is well motivated for NISQ
quantum adversaries and may remain plausible in the presence of early fault-tolerant
quantum computers. Then, we show that an efficient simulation-secure one-time
memory (OTM) is possible under CAROM. Our protocol uses only BB84 states and has quadratic communication: for a $\lambda$-bit message and integer-valued parameters $n=n(\lambda)$ and $\ell=\ell(\lambda)$, the construction uses $n\ell$
qubits and $(n+2)\lambda$ classical bits, and for any
quantum adversary with at most $2^{\ell/2-1}-1$ classical queries to the random oracle, its simulation advantage is at most $(n+3)\left(\frac{3}{4}\right)^n.$ Hence exponentially small simulation advantage in $n$.
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Machine Learning
AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion
Gal Wertheizer, Rom Himelstein, Tomer Peretz et al. (+1 more)
Adversarial attacks optimized on a single open-weight LLM can transfer to and jailbreak architecturally different models, allowing an attacker with white-box access to one model to compromise independently deployed systems. This creates a shared vulnerability across models, yet existing defenses are not designed for this cross-model threat. We find that cross-model transfer aligns with shared internal representation geometry, making it a natural defense target. AnchorRep targets this geometry directly with a lightweight LoRA adapter that pushes the defended model's internal representations of harmful
prompts away from those of a frozen anchor model on the same
prompts. Training uses a small set of harmful
prompts and no
adversarial examples. Across five models and four architectural families, AnchorRep reduces cross-model attack success rate to <=1.1% on 2,000 transferred attacks (0% on two), including the largest drop on Mistral (36% -> 1.1%). Existing defenses can reduce transfer, but only at high cost either inducing up to 77% degenerate benign output or increasing over-refusal by up to 18%. Because such degenerate benign outputs are not captured by standard refusal-based metrics, we introduce the Benign Garble Rate to quantify them. Our results suggest that cross-model robustness can be achieved by shaping representation geometry, without requiring attack-specific training
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All authors Gal Wertheizer, Rom Himelstein, Tomer Peretz, Avi Mendelson
Cryptography and Security
VulContextBench: A Benchmark for Security Context Retrieval in Coding Agents
Yikun Li, Jinfeng Jiang, Yuheng Yieh et al. (+6 more)
Vulnerability-detection
benchmarks score the verdict an
agent reaches, not the evidence it gathered. A model that recalls a CVE from pretraining therefore scores the same as one that traced the data flow. We study a task where this difference matters, deciding whether a commit introduces a vulnerability. Instead of scoring the verdict, we score whether the
agent retrieved the code its conclusion depends on. We present VulContextBench, a
benchmark of 111 vulnerability-introducing commits (VICs) across 83 repositories, 63 CWEs, and five
languages. Existing
datasets label such commits by tracing a fix back through the version history, which often points to the wrong commit. We therefore audit every case by hand against an explicit four-criterion definition of a VIC, so the
benchmark does not inherit that label noise. Each case is annotated with gold context, 464 code blocks in total, each tagged by its role in the evidence for the vulnerability. We evaluate seven frontier models with precision, recall and F1 at three granularities (file, block, and line), scored separately on the context an
agent viewed while exploring and on the context it finally declared as evidence. The gap between the two is the main finding. Every model opens most of the gold context while exploring, but reports only part of it as evidence. At the level of code blocks, the share of the gold context a model reports is 37 to 73 percentage points below the share it viewed. Qwen3-Coder-Next views 86.3% of the lines in annotated code blocks but cites only 12.9% in its final report. GPT-5.5, which cites the most, views 73% and reports 36%. These results highlight a gap between finding relevant code and selecting it for the final report, which verdict-level
benchmarks cannot reveal.
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All authors Yikun Li, Jinfeng Jiang, Yuheng Yieh, Ting Zhang, Yide Yin, Wen Bin Leow, Eng Lieh Ouh, Lwin Khin Shar, David Lo
Artificial Intelligence
CUA-SWE: When Computer-Use Agents Meet Visual Software Engineering
Prince Zizhuang Wang, Chenhao Liang, Zelong Xu et al. (+6 more)
Software development requires more than editing code: developers repeatedly run software, interact with its interfaces, visually inspect its behavior, and use these observations to decide what to change next and whether a change works. Existing coding
agents and computer-use
agents are largely studied in isolation, leaving this integrated development process underexplored. Diagnosing a runtime interaction failure requires
agents to connect visual observations with the responsible code, then use the application again to verify the repair. We introduce CUA-SWE, a
benchmark, environment, and evaluation pipeline for software engineering with computer use. Beyond studying how GUI feedback supports diagnosis and repair, we ask whether
agents can complete software engineering tasks when required specification or operational information is available only through the running application's visual interface. CUA-SWE spans four software engineering domains and requires
agents to modify code and configuration, execute commands, interact with running software, and inspect visual feedback within the same task. Each task includes deterministic, task-specific tests that verify whether the resulting software satisfies the requirements and preserves specified behavior. Our evaluation characterizes how frontier
agents combine source-level execution with application screenshots and graphical interaction to produce verified software changes. We examine performance across domains and task information requirements, alongside the development behaviors associated with successful repairs. CUA-SWE provides a unified testbed for studying how
agents use visual feedback and interaction to guide software engineering, with executable correctness criteria for the resulting software.
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All authors Prince Zizhuang Wang, Chenhao Liang, Zelong Xu, Aojie Yuan, Xiaolin Zhou, Haiyue Zhang, Yue Zhao, Xiyang Hu, Shuli Jiang
Machine Learning
Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression
Boxiao Wang, Kai Li, Yuheng Jing et al. (+4 more)
Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symbolic structures remains challenging. Existing methods typically guide this process using expression-level objectives, such as fitting error, which assess a candidate equation as a whole but provide little information about whether an individual term contributes genuinely new structure or is largely redundant with the rest of the expression. We introduce Sobolev Novelty, a term-level measure of structural independence for symbolic equations. For each term, we construct an empirical Sobolev signature from its function values and exact derivatives over the observed inputs, and quantify how much of this behavior cannot be reconstructed by the remaining terms. We further derive a theory-calibrated threshold, yielding a principled and tuning-free criterion for identifying structurally novel terms. Using this threshold, 92.6% of terms in
benchmark ground-truth equations exhibit sufficient structural novelty, compared with only 38.3% on average for expressions produced by 15 SR methods, revealing a substantial gap between scientific equations and current SR solutions. As a lightweight plug-in, Sobolev Novelty can be incorporated into diverse SR paradigms to support term
pruning, search guidance, LLM feedback, and data selection, yielding consistent performance gains and demonstrating broad applicability.
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All authors Boxiao Wang, Kai Li, Yuheng Jing, Tianyi Liu, Chen Li, Yifan Zhang, Jian Cheng
Computer Vision
GAUGE: Group-Wise View-Inconsistency Rectification for Feed-Forward 4D Tracking
Zhuoqian Feng, Weixing Chen, Ziliang Chen et al. (+2 more)
Feed-forward models regress dense 3D point trajectories directly from
monocular video, yet the residual after global
alignment is substantial and lacks a structural explanation. Measured on dynamic query points across models and
datasets, the error concentrates along the view direction, while the scale correction each motion group requires differs. The predicted displacement direction nevertheless supports reliable grouping, with a median angle far below the 90{\deg} random baseline. The systematic part of the residual is therefore a family of radial degrees of freedom per motion group, along directions 2D observations cannot constrain. We call it group-wise view inconsistency. We present GAUGE (Group-wise Adaptive Unsupervised Gauge Estimation), a training-free and model-agnostic post-hoc module. It recovers motion groups from direction consistency and spatial connectivity, then estimates a per-frame radial scale and group-level translation from 1% to 5% metric anchors, four degrees of freedom per group and frame. On dynamic query points of eight trackers, including D4RT, 4RC and SM4RT, our correction lowers endpoint error by 15.1% to 62.6% over the uncorrected predictions, while spending the same anchors on gradient fine-tuning improves the same models by only -1.1% to 15.2%. Code is publicly available at https://github.com/HCPLab-SYSU/GAUGE.
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All authors Zhuoqian Feng, Weixing Chen, Ziliang Chen, Yang Liu, Liang Lin
Robotics
RECAST: Recasting Vision-Language Semantics into an Actionable Cost Map for Robot Navigation
Incheol Cho, Jintae Park, Jinkyu Kim et al. (+3 more)
Safe and robust
robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stable motion. Recent works tackle this with learning-based models trained at scale and with approaches built on
vision-
language models (VLMs). However, learning-based models break down outside their training distribution, while VLM-based approaches bring that understanding but rarely ground it in the scene or align the action with it. To address these limitations, we present RECAST, a
robot navigation framework that combines the
reasoning of a VLM with the spatial grounding of
vision foundation models to build an Actionable Cost map. Given the
robot's front view and the user's instruction, we first decompose the scene with the VLM, judging which surfaces are traversable, which objects pose a risk, which heading to prefer, and which gaps are passable.
Vision foundation models then ground these surfaces and objects in the image, and all four judgments are spatially recast into one compact cost map. This map both conditions the trajectory decoders and scores their proposals to select the one to execute. As the VLM's answers trail the live scene, both steps draw on cost maps from two points in time: the pivot frame the VLM judged, which carries all four judgments, and the current frame, whose terrain and collision costs are rebuilt from the current image. RECAST improves success over the strongest prior method by 13.3 points in simulation and 31.4 points on a real quadruped, and reduces the collision rate relative to it by 9.6 and 14.3 points, reaching the lowest collision rate among all methods. The project page is available at https://recast-nav.github.io/
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All authors Incheol Cho, Jintae Park, Jinkyu Kim, Jungbeom Lee, Jaegul Choo, Seokha Moon
Artificial Intelligence
MA-FPPO: Multi-Agent Flow-Pretrained Policy Optimization
Guowei Zou, Haonan Chen, Haitao Wang et al. (+3 more)
Multi-
agent flow policies learn cooperative behavior from fixed offline
datasets, but often struggle to complete tasks in situations not covered by the offline data. In these situations,
agents must both adapt to changes in the environment and coordinate with one another, yet action patterns learned offline are often insufficient for effective adaptation and coordination. To address this problem, we propose Multi-
Agent Flow-Pretrained Policy Optimization (MA-FPPO), which uses online fine-tuning to improve the cooperative behavior of models pretrained with flow matching through new interactions with the environment. Building on the behavior learned during pretraining, we construct policies with explicit action likelihoods for discrete and continuous action spaces. We then update the pretrained model using shared team advantages to further improve coordination based on team performance. Our method achieves, on average, relative gains of 52.8% over the strongest listed offline baselines across 30 settings and 29.8% over purely online learning across 38 comparisons with matched online budgets and evaluation protocols.
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All authors Guowei Zou, Haonan Chen, Haitao Wang, Beiwen Zhang, Na Yan, Hejun Wu
Machine Learning
Age of Learning: Temporal Persistence of Prediction Errors as a Learning Signal
Chenyang Wang, Stefan Forsström, Roger Olsson et al. (+2 more)
Current machine learning
algorithms primarily rely on instantaneous signals such as loss, margin, and prediction confidence to characterize model behavior. These signals indicate how difficult a prediction is at the current optimization step, but they do not capture how long the model has remained incorrect. We study this temporal dimension of learning and introduce Age of Learning (AoL), a learning-state variable that measures the persistence of prediction errors over time. AoL increases while an error remains unresolved and resets when a correct prediction is achieved, thereby distinguishing persistent under-learning from transient mistakes. We develop AoL-based training strategies for both offline and streaming settings. In offline learning, sample-level AoL is accumulated over training and aggregated into class-level states that guide adaptive reweighting and resampling. In streaming learning, where full historical access is unavailable, we maintain lightweight class-level AoL states using current and buffered observations. Across long-tailed classification settings, AoL improves or matches standard training baselines, with larger benefits when learning difficulty persists over time. Multi-seed streaming experiments further show reproducible gains under temporally stable imbalance. Analysis of class frequency, loss, and margin shows that AoL is related to conventional difficulty measures but captures additional information about error duration. These results suggest that temporal persistence provides a useful complementary signal for characterizing and controlling learning dynamics in imbalanced and non-stationary environments.
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All authors Chenyang Wang, Stefan Forsström, Roger Olsson, Di Yuan, Qing He
Computer Vision
SPACE: Sparse Predictive Attractor via Counterfactual Eviction for Streaming Video Memory
Hongjin Niu, Weizhan Zhang, Shuo Bao et al. (+4 more)
Fixed-capacity streaming
video memory requires repeated eviction decisions whose effects accumulate over time. Yet existing policies are evaluated primarily in terms of retained information or downstream accuracy, leaving how repeated updates alter the futures supported by
memory largely unexamined. We define a
memory's predictive state as the future representations supported by its retained history and formulate eviction as counterfactual control over transitions in this space. We introduce SPACE (Sparse Predictive Attractor via Counterfactual Eviction), which uses a frozen multi-horizon JEPA to predict the future representations induced by alternative eviction actions. Counterfactual utility identifies future-useful alternatives, while slow predictive-basin geometry determines when to correct avoidable drift and when to adapt to sustained predictive change, without online parameter updates. We further introduce MABS-Bench, which evaluates future-task sufficiency, within-regime predictive stability, transition responsiveness, and perturbation recovery under matched causal streams and
memory budgets. Across multiple
video datasets, SPACE yields consistent improvements in
dataset-native task performance while reducing predictive-state drift.
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All authors Hongjin Niu, Weizhan Zhang, Shuo Bao, Jiahao Wang, Muyan Jiao, Kairui Wen, Yong-Jin Liu
Robotics
Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC
Yi Xian Goh, Sze Jue Yang and Hao Luan
Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuous control. However, the per-step cost of sampling and evaluating hundreds of candidate trajectories restricts deployment to control frequencies well below what real-time robotics demands. Motivated by the dual-process theory of human cognition, which distinguishes between fast, intuitive processing (System 1) and slower, deliberative
reasoning (System 2), we ask whether every decision requires the same degree of computational deliberation. We propose Fast-TD-MPC, a lightweight framework that adaptively routes between fast policy execution and test-time planning, reserving costly deliberation for states where it is most needed. Fast-TD-MPC delivers competitive task performance across 103 continuous control tasks while achieving up to ~4x faster inference. Under external disturbances, Fast-TD-MPC selectively falls back to planning, maintaining robustness comparable to the original planner.
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