Artificial Intelligence
Risk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets
Priyanath Maji and Spandan Ghose Chowdhury
Evaluating interactive
agents is expensive.
Agent behavior is
stochastic, so reliability must be measured over repeated trials, but failures are rare and differ widely in how much they matter. Standard
benchmarks spend this budget uniformly: a read-only lookup is sampled as often as an irreversible payment action. We instead formulate evaluation as a sequential allocation problem. Given a fixed trial budget and a set of scenarios whose failure behavior is unknown, which scenarios should be run, and run again? We propose a risk-aware contextual Thompson Sampling policy that combines a pre-execution scenario context vector and a fixed impact score with the failure outcomes observed during evaluation, and we test it by offline replay over 70 $\tau$-bench airline scenarios and 824 recorded trials. Our main result is at the smallest budget: with only 50 trials ($6\%$ of the corpus), the policy recovers $86\%$ of the impact-weighted failures an oracle could find, compared to $25\%$ for uniform allocation. It discovers $3.5\times$ more impact-weighted failures (215.4 vs. 62.2) with the same number of trials, delivers $5\times$ the discovery per dollar, and cuts the budget wasted on scenarios that never fail from $34\%$ to $2.8\%$. The rest of our analysis demonstrates and qualifies this result: a budget sweep shows the advantage shrinks as the budget approaches the corpus size, and paired significance tests show that scenario context helps mainly at small budgets while posterior-based exploration helps at moderate ones. Risk-aware adaptive allocation therefore helps most exactly where evaluation budget is scarcest.
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Computer Vision
FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement
Bo Zhao, Junzhe Cao, Dan Guo et al. (+5 more)
Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains vulnerable to domain shifts from illumination, motion, and sensors. We propose FLOW (Feature-Level Optimal Warping), an optimal transport–driven framework for domain-generalized rPPG. FLOW integrates a Temporal Refinement Module (TRM) to stabilize temporal dynamics and a Prototype-based Cross-Temporal Optimal Transport (PCOT) module to achieve domain-invariant
alignment via learnable prototypes.Beyond feature
alignment, FLOW employs soft cross-temporal correspondence modeling that aligns temporal features in a flexible manner, allowing the model to respect and preserve the intrinsic rhythmic patterns of physiological signals. Moreover, the lightweight design of our modules allows seamless integration into existing end-to-end rPPG architectures without additional preprocessing. Two regularization terms further enforce source consistency and identity preservation. Theoretically, we derive a generalization bound under conditional optimal transport. Extensive experiments across four rPPG
benchmarks show that FLOW achieves state-of-the-art cross-domain performance with lightweight design and strong physiological fidelity.
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All authors Bo Zhao, Junzhe Cao, Dan Guo, Dongmin Huang, Wenjin Wang, Tao Tan, Yue Sun, Zitong YU
Artificial Intelligence
Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives
Peng Kuang, Haibo Jin, Dehao Wu et al. (+4 more)
Agent harnesses govern how large
language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting
agent performance. However, the value of each harness mechanism can differ across heterogeneous tasks: a mechanism that improves one task may impose overhead or context distraction on another, leading to the suboptimality of a global harness. We characterize this suboptimality as a mismatch induced by fixed mechanism choices, motivating task-specific harness construction. Nonetheless, generating harness code for each task introduces generation and debugging costs, with execution risks that can compound as more mechanisms are generated. To address those challenges, we introduce Harness Primitives, reusable harness mechanisms with clear application scope and composition contract mined from failed task trajectories. Based on Harness Primitives, we propose STITCH, a framework that Selects suitable primitives given Task Information and compiles them into Task-speCific Harnesses at test time. This separation enables task-specific harnesses without generating or repairing mechanism code at test time. Extensive experiments demonstrate that STITCH not only improves harness adaptability and robustness, but also scales with the primitive library size, boosting task success rates by up to 12 points over fixed harness baselines, surpassing human-designed harnesses like Codex CLI while maintaining a minimal test-time harness composition overhead of only 2.7%, 638 times more efficient than generating task-specific harnesses from scratch. Ultimately, our work demonstrates that building task-adaptive harnesses can be beneficial for completing diverse tasks and that building reusable primitives can be a promising path towards this goal.
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All authors Peng Kuang, Haibo Jin, Dehao Wu, Feiyang Deng, Xiaopeng Yuan, Jerry Wang, Haohan Wang
Machine Learning
Generalized Residual Closure: General Learning Dynamics for Stability-Plasticity Compatibility
Dongxu Li, Yinuo Zhang, Hongyu Zhang et al. (+1 more)
Learning must acquire new capabilities while preserving both prior responsibilities and the capacity to learn again. We introduce Generalized Residual Closure (GRC), a framework for learning as recursive closure of future-relevant discrepancies: closing a residual establishes the conditions for subsequent prediction, interaction, and learning. Under a complete representation-relation description at a fixed learner-world boundary, persistent internal learning has two primitive modes: Transformation within a representation and revision of the Representation itself. We establish a local tangent decomposition under regularity assumptions and a criterion for when representation revision is necessary. In an affine model, we derive a necessary-and-sufficient condition for stability-plasticity compatibility and the unique solution of a constrained quadratic update problem, which preserves registered old responsibilities while reducing residuals with an effective safe response. We prove that reconstructive semantic protection weakly enlarges the safe-response operator relative to preserving an exact historical realization. Dynamic sufficiency and future-closure viability extend representation adequacy from current prediction to lawful future updating and continued learning. Growth Learning expands the lawful closure domain or lowers optimal closure cost without regression of the registered capability-cost frontier; a conditional commit rule maintains this order. Restricted-sector recoveries and a conditional representation
theorem connect the framework to optimization, machine learning, and control. Together, these results organize adaptation, representation revision, and reusable capability within a common account of continued learning.
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All authors Dongxu Li, Yinuo Zhang, Hongyu Zhang, Feng Tian
Computational Complexity
Frustration Free Stoquastic Local Hamiltonian with Sub-Constant Gap is in NP
Eshan Chattopadhyay, Oren Renard and Nicholas Spooner
We continue the study of the Stoquastic Local
Hamiltonian problem, a physically motivated restriction of the QMA-complete Local
Hamiltonian problem (Kitaev, Shen, and Vyalyi, 2002). For the $\beta$-gapped, frustration-free case, Bravyi, Bessen, and Terhal (2006) showed that the problem is MA-complete when $\beta = 1/\mathrm{poly}(n)$. Aharonov and Grilo (2019) derandomized this
algorithm and proved membership in NP for constant gap $\beta = \Omega(1)$. We present an improved
algorithm and analysis, establishing membership in NP even when $\beta = \Omega(1/(\log\log n))$. We complement our result with an explicit example demonstrating why the analysis does not extend directly to $\beta = o(1/\log\log n)$.
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Machine Learning
Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets
Haoran Tang and Rajiv Khanna
Large
language models often know the truth and say otherwise: a model that answers correctly when asked neutrally will affirm a user's mistaken belief, or misstate a fact its system
prompt wants hidden, once the context rewards it. Such deception is a behavior conditioned on context, not knowledge, yet machine unlearning, the natural tool for removing a behavior from the weights, is built to forget facts that a deceptive model still needs. We propose to unlearn when a model deceives rather than what it knows, with a contrastive forget unit built from the model's own realized deceptions: the same question under a deception-triggering and a neutral context, admitted only where belief holds and behavior flips. Standard objectives on this unit face a dilemma. Suppression objectives such as NPO leave much of the deception in place. Target-based objectives, which distill the model's neutral behavior into the pressured context, remove it but induce context blindness: a target generated without the context teaches the model to stop reading it, eroding benign system-
prompt instructions, secret-keeping and the
reasoning a monitor inspects, a failure invisible to deception rates and capability
benchmarks. We introduce PACT, which trains toward pressure-aware counterfactual targets (the model's own honest response, with a trace that registers the pressure and resists it) while retaining the benign uses of the triggering context. On two 32B
reasoning models, PACT reduces held-out deception from over 50% to under 3% while system-
prompt adherence, secret-keeping and the
reasoning trace stay at the base model's level. On a tug-of-war score of removal against retention, PACT reaches 0.94 and 0.86, against at most 0.77 and 0.60 for any baseline. Like removed knowledge, removed deception is shallow under relearning, and terms that simulate the attacker hold it only at a cost in context use.
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Artificial Intelligence · Cross-listed from q-bio.GN
CellMSA: Context Modeling for Single-Cell Representation Learning
Suyuan Zhao, Minghao Liu, Yizhen Luo et al. (+1 more)
Single-
cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical batch effects pose significant challenges for representation learning. Existing single-
cell foundation models typically encode each
cell independently or only model
cells from the same batch for denoising, thereby underutilizing the rich relational information across batches and
cell types to model gene expression patterns. We argue that single-
cell models can benefit from more informative
cell-context modeling. By comparing consistency and variation across
cells, models can capture fine-grained gene-gene dependencies associated with
cell states, which are essential for learning high-quality representations. Inspired by the use of multiple sequence
alignment (MSA) context in
protein modeling, we propose CellMSA, a single-
cell representation learning framework that introduces an MSA-inspired inductive bias into transcriptomic modeling. For each target
cell, CellMSA retrieves relevant
cells from different batches and biologically related
cell types as context, and summarizes cross-
cell patterns into a context-dependent gene-pair representation. This representation is then injected into a pair-aware target-
cell encoder for fine-grained representation learning. We pretrain CellMSA on a large-scale human single-
cell corpus of approximately 109 million
cell observations, including 65.6 million primary observations. Experiments show that our framework consistently outperforms existing methods across multiple
benchmarks. Code is available at the following repository: https://github.com/PharMolix/CellMSA.
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All authors Suyuan Zhao, Minghao Liu, Yizhen Luo, Zaiqing Nie
Human-Computer Interaction
Characterizing Questioning Patterns and Student Engagement Through Contextual Analysis of Real-Time Classroom Interactions
Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V. et al. (+5 more)
Real-time classroom polling is now routine, yet the data it produces is usually read narrowly, as a correctness score or a headcount. Such readings say little about what a poll is doing within a lecture or how it shapes engagement. This is particularly relevant for short-response formats such as True/False, where the same question format can be used to test recall, check comprehension, or direct students'
attention to a deliberately misleading statement. This study asks whether a poll's answer and instructional function can be determined by reading it against its lecture transcript, what cognitive levels of Bloom's taxonomy and instructional-function clusters the corpus contains, and how student engagement relates to answering correctly. We analyse a naturalistic corpus of 47 live sessions over 39 days, comprising 604 poll questions and 340,668 responses from 2,807 learners, most items True/False, read against time-aligned lecture transcripts and attendance. Reading each poll in context proves essential: the answer to 89% of polls is locatable in the lecture, and a recurring
attention-checking device is visible only through context. Questioning is overwhelmingly lower-order and falls into seven instructional functions, and a poll's response follows its function rather than its wording. Engagement is broad but concentrated, and the class majority answers correctly 88.5% of the time, though a small set of high-consensus yet incorrect answers cannot be detected by agreement alone. An independent survey of 579 students agrees on what the polls are and on their participation, but reveals a gap between perception and reality: students cannot judge their own correctness, and the polls they find hardest are not those they answer worst.
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All authors Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V., Jinal Gupta, Prakash Hegade, Sakshi Sharma, Meenakshi V, SRS Iyengar
Robotics
EmbodiRSI: Recursive Self-Improvement for Data-Efficient Robot Adaptation
Haoran Lang, Haotao Lu, Shiyu Sang et al. (+6 more)
Adapting
robot manipulation policies to new tasks and environments remains highly data-intensive, while the data needed for further improvement depends on the policy's current capabilities and failure modes. We introduce EmbodiRSI, an agentic system for recursive self-improvement (RSI) in a real-to-sim-to-real setting, where task-specific simulations are constructed from target deployment scenarios and used as low-cost environments for iterative policy improvement before transfer back to the physical world. EmbodiRSI uses policy execution feedback to guide subsequent experience acquisition and policy updates. Two complementary mechanisms close this loop: Collaborative Error Correction generates
agent-assisted corrective trajectories from policy-reached states, while Adaptive Data Collection directs expert demonstration generation toward the current policy's weaknesses. The task-specific simulation serves as a reusable workspace for policy warm-up, repeatable evaluation, failure diagnosis, and targeted data generation across successive RSI rounds. Across three tabletop environments and 14 subtasks, EmbodiRSI increases scene-balanced autonomous simulation success from 50.4% to 83.5% over two RSI updates. With 400 adaptive simulated trajectories and only ten real-world refinement trajectories per subtask, EmbodiRSI achieves 83.1% scene-balanced autonomous real-world success, compared with 75.0% for adaptation using 200 real-world demonstrations per subtask. These results demonstrate that feedback-driven recursive improvement in deployment-specific simulations can enable data-efficient adaptation of
embodied policies to physical environments.
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All authors Haoran Lang, Haotao Lu, Shiyu Sang, Haoyang Luo, Guo Chen, Qun Li, Jingyi Yu, Ye Shi, Jingya Wang
Machine Learning
DAMPER: Return-Prioritized Gradient Control for Smooth Policies
Seokmin Ko, Taewon Goo and Kihyuk Hong
Actor-critic methods achieve strong performance in continuous control, but their policies can produce highly oscillatory actions. A common remedy is to add auxiliary smoothness losses. However, their contribution can be negligible when their gradients are small relative to the native actor gradient. Moreover, existing methods often combine multiple auxiliary losses, complicating loss balancing without necessarily improving the return-smoothness trade-off. We introduce DAMPER (Direction-Aware Magnitude-Controlled Projection with Explicit Return Priority), which combines the native actor gradient with a temporal-consistency gradient through conflict-conditioned projection and adaptive magnitude control. It removes the auxiliary component opposing the actor gradient and scales the retained temporal direction relative to the actor gradient norm, preserving positive
alignment with the native actor gradient. Experiments with TD3 and SAC on six continuous-control tasks show reduced action oscillation relative to the native
agents in all 12 task-backbone pairs and the best oscillation score among the compared methods in eight, with task-dependent return trade-offs.
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Machine Learning
Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation
Chin-Hung Huang, JooChul Lee and Huan He
Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is expensive, and closely related trial or real-world data are often already available.
Bayesian dynamic borrowing (BDB) provides a principled framework for adaptively controlling the influence of external data, but classical implementations often depend on hand-specified priors and MCMC-based inference, which can be computationally expensive and not generalizable. In this work, we study amortized neural posterior estimation (NPE) as a flexible alternative. A single network is pretrained on simulated current/external
dataset pairs spanning
covariate shift, outcome drift, and joint non-exchangeability, and then returns an approximate posterior for a scalar current-study target in a single forward pass. Through simulation studies, we find that NPE is most useful under outcome drift and joint mismatch: in the harder outcome-drift regimes, it gives up to about five-fold lower absolute bias than the best classical baseline and keeps Type I error close to nominal. After pretraining, posterior summaries are obtained in about 8 ms per
dataset, roughly $10^3\times$ faster than MCMC-based borrowing baselines in our timing experiment. We further analyze Alzheimer's Disease Neuroimaging Initiative (ADNI) data and show that, when mild cognitive impairment outcomes differ across cohorts, the NPE formulation recovers the later-cohort risk level in this example without claiming greater precision. Code is available at https://github.com/ChinHungScott/NPE-for-
Bayesian-Dynamic-Borrowing-MLHC-.
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Machine Learning
Certified Approximation for Interpretable Representer Landmarks
Jayanta Mukherjee, Shourya Verma, Mengbo Wang et al. (+2 more)
Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four stacked approximations of the empirical neural tangent kernel (eNTK). These are random output heads, a parameter sketch, landmark sampling and a coefficient fit. Existing analyses bound each approximation separately, but none certifies the top-$K$ set against their combined error. We introduce CAIRN (Certified Approximation for Interpretable Representer laNdmarks), a framework that carries this error through to the ranking. We derive the exact variance of the sketched multi-head eNTK, which matches measurement within $4\%$ where Johnson-Lindenstrauss bounds err by up to $2.5\times$. This yields a high-probability top-$K$ certificate for a fixed coefficient fit, alongside exact residual-trace certificates for discarded spectral mass. An exact product-variance identity separates kernel error from fit variability and identifies when a larger kernel budget can still sharpen a ranking.
Stochastic Lanczos Quadrature (SLQ) estimates the effective dimension within $0.72\%$ and guides the landmark budget without dense eigendecomposition. We show that residual mass does not control class coverage, and residual-greedy selection cuts the worst coverage excess of $k$-means++ from $8.5\times$ to $1.55\times$ ($4\times$ on the sketched eNTK). Cross-view initializers outperform principal-component initialization in five (AUI) to all six (CSI) settings. Against the KREPES Gauss-Newton solver, CAIRN converges $2.5$ to $11.3\times$ faster, trails by at most $0.31$ points and gains up to $3.14$ points on MNIST. Together, these results make the reliability of representer explanations measurable and show where approximation budgets are best spent.
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All authors Jayanta Mukherjee, Shourya Verma, Mengbo Wang, Jasorsi Ghosh, Ananth Grama
Computer Vision
MEMO: Multi-Level Entity-Aware Memory for Streaming Video Understanding
Yinying Li, Yuqian Fu, Yulin Dai et al. (+3 more)
Streaming
video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons, posing a fundamental challenge for
memory modeling. Existing approaches primarily focus on increasing
memory capacity, either by compressing historical information into fixed-size representations or by extending storage beyond GPU
memory. However, these methods largely rely on global or coarse-grained representations, inevitably losing fine-grained visual information. In this work, we argue that streaming
video memory should explicitly encode structured and semantically meaningful representations, particularly at the entity level. To this end, we propose MEMO, a novel framework that models streaming
video through multi-level, entity-aware structured
memory. MEMO performs multi-level perception to jointly capture global semantics, entity dynamics, and spatial structures, partitioning streaming
video into semantically coherent chunks. Each chunk is organized into a structured
memory, where lightweight global and entity-level representations serve as
retrieval indices, while the corresponding high-resolution visual content is retained separately for on-demand access. At inference time, MEMO performs query-specific
retrieval over the structured
memory and selectively recalls relevant visual evidence for downstream
reasoning. Notably, MEMO is training-free and plug-and-play with existing
multimodal large
language models. Extensive experiments on StreamingBench and OVO-Bench demonstrate that MEMO consistently improves multiple base models and achieves state-of-the-art performance. The code is available at https://github.com/Cecilia384/MEMO .
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All authors Yinying Li, Yuqian Fu, Yulin Dai, Jingyu Gong, Tianwen Qian, Xiaoling Wang
Cryptography and Security
SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
Wenyu Chen, Li Wang, Chuanchao Zang et al. (+5 more)
Video Multimodal Large
Language Models (
Video-MLLMs) support
reasoning over
video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existing
video jailbreaks primarily manipulate how harmful queries are visually presented, thereby treating
video merely as a carrier. Consequently, the surrounding
video scenario remains unexplored as a contextual attack surface. In this paper, we show that the same harmful query can elicit different safety responses when placed in different
video scenarios. To systematically exploit this vulnerability, we propose SceneJail, an adaptive black-box jailbreak framework with two coordinated components. Adaptive Scenario Construction dynamically searches for a surrounding scenario that is contextually compatible with the harmful query. Scenario-aware
Prompt Search uses black-box response feedback to search for textual guidance tailored to the selected scenario. Extensive evaluations on the HADES and SafeBench
datasets across eight
Video-MLLMs, including two proprietary models, GPT-4.1 and Gemini3.5-Flash, demonstrate the effectiveness of SceneJail. SceneJail-F, which presents the complete query persistently, achieves average attack success rates (ASR) up to 91.5%, outperforming the strongest baselines by 29.1 percentage points. Furthermore, SceneJail-S, which distributes the query across successive frames, remains highly robust against current defenses, retaining a 72.3% ASR even under strict image filtering.
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All authors Wenyu Chen, Li Wang, Chuanchao Zang, Xiangtao Meng, Xinyu Gao, Jianing Wang, Zheng Li, Shanqing Guo
Machine Learning
Yingchuan Zhang, Haoran Lu, Wenxuan Zhong et al. (+1 more)
Knowledge
distillation can transfer
reasoning from stronger teachers to frozen students through reusable
prompts, but avoiding weight updates does not eliminate supervision. Without ground-truth answers, teacher solutions are unverified, and agreement with the teacher can reward shared mistakes. We introduce Knowledge-to-
Prompt (K2P) for label-free knowledge
distillation to
prompts. K2P synthesizes reusable instructions from teacher solutions, refines them using paired teacher and student responses, and guides search and selection with answer agreement. It retains candidates that adaptive search may undervalue and selects on reserved questions. Deployment uses only the frozen student and selected
prompt. Our theory separates generation and selection gaps and gives conditions under which agreement-guided construction yields accuracy guarantees despite imperfect teacher references. Across
reasoning tasks and students, K2P outperforms label-free alternatives overall and remains competitive with supervised
prompt optimization. Ablations and archive diagnostics assess the contributions of teacher solutions and refinement, while revealing the limits of agreement-guided selection.
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All authors Yingchuan Zhang, Haoran Lu, Wenxuan Zhong, Ping Ma
Sound
FFASR: Benchmarking Far-Field Automatic Speech Recognition using High-Fidelity Simulated RIRs
Shivam Saini, Eric Bezzam, Georg Götz et al. (+5 more)
Far-field automatic
speech recognition(ASR) degrades under reverberation, noise, and talker motion, yet the
benchmarks that drive model selection emphasize close-microphone
speech. We present FFASR, a held-out corpus of 15,637 utterances and an open leaderboard spanning nine conditions, each varying a single acoustic factor: anechoic near-field
speech, a measured-versus-simulated office-lab pair, static far-field mixtures at high/mid/low signal-to-noise ratio(SNR), and moving-talker variants at matched SNR. Dry
speech from 15 talkers is convolved with hybrid wave/geometrical-acoustics room impulse responses from 14 furnished rooms; because the
speech is newly recorded and the test waveforms are never released, the corpus resists training-data contamination. Across contemporary systems, mean word error rate (WER) rises from 4.4% near-field to 41.3% in the static low-SNR condition; a moving talker adds a small but consistent penalty at matched SNR; and on the office-lab pair, measured and simulated WER agree to within about 1.7 pp on average. These results support high-fidelity simulation as a scalable proxy for measured far-field evaluation under the conditions we test.
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All authors Shivam Saini, Eric Bezzam, Georg Götz, Alessia Milo, Steinar Gu{\dh}jónsson, Konstantinos Gkanos, Finnur Pind, Daniel Gert Nielsen
Machine Learning
Unmerge: Efficient Machine Unlearning via Task Arithmetic
Haoran Tang, Andrew Tan and Rajiv Khanna
Approximate machine unlearning seeks to remove the influence of a forget set from a trained model without full retraining. Existing gradient-based methods require data-dependent hyperparameter search, struggle when forget and retain knowledge are entangled, and offer little insight into where unlearning actually happens inside the network. We recast unlearning through the lens of task arithmetic: if finetuning produces a merged task vector $\tau_m$ that combines learning on forget and retain sets, unlearning is the inverse operation that subtracts a learned forget component $\tau_F$ to recover the retain task vector $\tau_R$. The forget signal is concentrated: at every layer, forget activations lie in a subspace spanned by a handful of dominant directions, so we factorize $\tau_F$ in a low-rank forget basis, which is faithful up to a small tail-
eigenvalue residual and limits how far the correction can perturb retain. We then optimize three intuitive goals (match the merged vector inside the forget span, suppress leakage into the retain span, and bound the correction size) that provably bound forget leakage and retain damage in activation space. The resulting
algorithm, Unmerge, is fast and powerful: on class-level unlearning with ResNet-50 on CIFAR-100 and Tiny ImageNet, it improves Tug-of-War by up to ~24% over a baseline of comparable runtime and by up to ~18% over stronger baselines that run ~5x slower, keeps membership-inference exposure at the level of retraining, and shrinks the feature-distribution gap to the retrained model, where relabeling methods leave forget features cleanly separable. Further studies show that Unmerge also applies to ViT-S/16 and scales to Llama-3.2-3B. The per-layer basis geometry that drives the
algorithm also serves as a layerwise diagnostic for when and where unlearning becomes structurally hard.
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Cryptography and Security · Cross-listed from quant-ph
Quantum Time-Lock Puzzles in the Quantum Random Oracle Model
Prabhanjan Ananth and Yao-Ting Lin
A time-lock puzzle allows a sender to hide a message in a puzzle such that recovering the message requires substantially more sequential computation than the time required to generate the puzzle, even when parallel computation is allowed. Applications of time-lock puzzles include timed-release encryption, sealed-bid auctions, electronic voting, fair contract signing, coin flipping, and Byzantine consensus. However, time-lock puzzles are known to be impossible in the classical random oracle model. To overcome the classical barrier, in this work we consider
quantum time-lock puzzles, in which the puzzle itself is a
quantum state. Our construction in the
quantum random oracle model achieves generation in one oracle round, solving in at most $T$ oracle rounds, and security against
polynomial-width
quantum adversaries of depth $o(T)$ for every polynomially bounded delay $T=T(\lambda)$, resolving an open problem posed by Mahmoody, Moran, and Vadhan (2011).
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Machine Learning
Learning Continuous Neural Representation of Stochastic Hybrid Systems
Sangli Teng, Hang Liu and Koushil Sreenath
A
stochastic hybrid system (SHS) is governed by a
stochastic differential equation (SDE) describing the continuous dynamics and a Markov reset kernel triggered on the guard surface. Its probability
evolution can be described by a hybrid Fokker-Planck (HFP) equation with a partial differential term corresponding to the SDE and an integral term arising from the reset kernel. This work shows that such an SHS can be approximated by an SDE in a higher-dimensional latent space where the sample paths are continuous. The key to this result is to encode different branches of the reset kernel using auxiliary variables, transforming the resets into deterministic ones that enable topological gluing. By the
embedding theorem, the glued
manifold can then be embedded into a higher-dimensional Euclidean space. We show that the probability
evolution on the embedded image no longer requires explicit reset terms in the HFP equation. Building on this
theorem, we design a loss that matches the evolving state distributions, enabling a single latent SDE to recover the probability
evolution of the SHS without mode labeling, trajectory
segmentation, or event-based simulations.
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Artificial Intelligence
Consistent Plan-Act for Long-Horizon Agentic Tasks
Heng-Zhuang Li, Yi-Kai Zhang, Yu Wang et al. (+4 more)
Long-horizon agentic tasks demand strong
reasoning and efficient execution across successive interactions with dynamic environments. A common approach decouples high-level planning from low-level execution through separate planner and actor roles. To investigate coordination failures in these tasks, we
prompt both
agents for structured state assertions and compare their reports programmatically to detect explicit contradictions. Our analyses reveal systematic disagreement about the same task-relevant state facts, a phenomenon we term planner-actor state mismatch. We further find that providing
agents with task-relevant state information reduces mismatch and improves coordination and task performance. Based on the systematic analysis of the state mismatch, we propose Consistent Plan-Act (ConPAct), which feeds detected contradictions back to both
agents to form consistent state interpretations and fine-tunes them on curated consistent interactions for better coordination. ConPAct improves performance across various environments and model configurations, e.g., increasing MiniGrid success rate from 38.6% to 54.4% with GPT-5.6-sol/terra as planner and actor respectively, demonstrating that state consistency can guide both inference-time correction and coordination training.
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All authors Heng-Zhuang Li, Yi-Kai Zhang, Yu Wang, Yueqing Sun, Jiayuan Zhang, Qi Gu, Han-Jia Ye
Robotics
PRICE the Action Chunks: Physical Relational Credit Assignment for Embodied Reinforcement Learning
Yangang Zou, Jiajun Lu, Weitao Zhou et al. (+6 more)
Outcome-based reinforcement learning (RL) post-trains
vision–
language–action policies using terminal success signals, but assigns the same trajectory-level advantage to every action chunk. A failed episode can thus penalize useful early actions as if they caused the failure. Existing approaches seek finer-grained feedback through learned evaluators, adding task-specific supervision or additional model training. We explore, for the first time to our knowledge, whether physical relations across trajectories can provide action-chunk credit in
embodied RL from terminal outcomes alone, without an auxiliary evaluator. The key insight is that rollouts reaching corresponding physical situations can serve as references for one another: their terminal outcomes provide evidence for assessing local progress. We introduce Physical Relations for Inferring Credit from Episodes(PRICE), with two components: (i) a physical relational graph that pools current and historical outcomes at corresponding chunk boundaries to estimate success potentials; and (ii) confidence-gated credit assignment that uses changes in these potentials to refine trajectory-level supervision. Our analysis connects oracle potential changes to the terminal-success objective and provides a finite-sample directional bound for outcome-independent evidence pools. Independent continuation tests show that PRICE's retained credits align with local progress, while experiments on LIBERO, RoboTwin 2.0, and real
robots demonstrate improved task success over outcome-based baselines and faster learning.
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All authors Yangang Zou, Jiajun Lu, Weitao Zhou, Haibao Yu, Bozhou Zhang, Jiawei Wang, Honglong Tian, Minglei Li, Li Zhang
Machine Learning
VERA: Verifiable Feasibility Representations with Counterfactual Credit for Constrained Multi-Agent Control
Bo Yin, Dongbo Li, Hongkai Chen et al. (+2 more)
Constrained multi-
agent control requires more than predicting rewarding actions: an action can cease to be executable as contact windows, shared capacity, and deadlines change. We introduce VERA, a centralized-training, decentralized-execution framework that separates feasibility estimation from credit assignment. Each actor predicts a five-dimensional verifiable feasibility representation (VFR). After an action is proposed, exact action-conditioned margins available only during training supervise that representation, while a counterfactual group-relative advantage (CGRA) ranks candidate representation-action pairs. Execution uses one actor pass and no privileged state. In a dynamic space-air-ground integrated network (SAGIN), VERA obtains 55.33% +/- 3.60% success with 0.45% +/- 0.81% coverage violation, within 1.33 percentage points of a privileged-mask reference. With rewards matched over ten paired seeds, VERA improves success over the strongest baseline by 8.74 percentage points (p=0.023) and reduces violation by 52.19 percentage points (p=5.7e-8). A ten-seed 4-by-2 factorial attributes a 14.16-16.48 percentage-point gain to CGRA across handcrafted, learned, random, and latent representations; evaluation on seven unseen
topologies preserves a 24.33-30.02 percentage-point advantage over multi-
agent proximal policy optimization. From 10 to 40 users, success remains 50.1-53.8%, and VFR adds only 0.026 ms to a central processing unit (CPU) actor step. Cross-domain tests further identify the governing condition: counterfactual credit succeeds when candidate scores respect shared constraints and fails under incompatible reward geometries. These results establish action-conditioned feasibility as an auditable training interface and counterfactual credit as a geometry-dependent optimization mechanism.
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All authors Bo Yin, Dongbo Li, Hongkai Chen, Jie Liu, Guoliang Xing
Computation and Language · Cross-listed from eess.AS
VOSSA: Voiceprint Optimization for Streaming Speech Architectures
Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah et al. (+1 more)
Real-time voice conversion (VC) systems commonly rely on pretrained speaker
embeddings from automatic speaker verification (ASV) models. While effective for speaker discrimination, these
embeddings are trained to remain stable across phonetic and prosodic variations within-speaker, which may conflict with frame-level acoustic generation in streaming constraints. To address this issue, we propose VOSSA (Voiceprint Optimization for Streaming
Speech Architectures), a speaker representation framework that extracts speaker information from intermediate content encoder layers and aggregates using attentive statistics pooling. The
embedding is trained jointly with VC objectives, removing the need for a separate speaker encoder. Across six
datasets, VOSSA improves F0 dynamics and vowel-discriminative acoustic cues while maintaining comparable NISQA-MOS, WER, and speaker similarity. Perceptual tests further indicate improvements in naturalness, speaker similarity, intelligibility, and vibrancy.
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All authors Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah, Ricardo Gutierrez-Osuna
Robotics
Benchmarking and Enhancing Skill-Level Memory for Partially Observable Robotic Manipulation
Yansong Shi, Jiange Yang, Xijie Yang et al. (+4 more)
Recent advances in
robot learning have enabled manipulation policies to perform increasingly diverse tasks and generalize across environments. However, reliable execution often depends on hidden task states that cannot be determined from current observations alone, making interaction history essential. We introduce $HIDE$, a
benchmark for evaluating manipulation
memory under partial observability. HIDE comprises 15 tasks covering repetition counting, historical-state recall, and execution-progress tracking, with randomized initial configurations and decision points where similar observations require different actions depending on prior events. We further propose $SEEK$, a framework combining three complementary
memory mechanisms to retain historical evidence and track execution state. Evaluations reveal substantial limitations in existing policies on HIDE, while
memory augmentation improves task success in both simulation and real-world experiments. Individual mechanisms benefit some tasks but can degrade others; their combination achieves the highest average success rate on HIDE among the evaluated configurations. These findings highlight the importance of maintaining internal representations of hidden task states and matching
memory design to task-specific information requirements.
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All authors Yansong Shi, Jiange Yang, Xijie Yang, Shaowei Zhang, Yuhan Zhu, Tao Lu, Limin Wang
Software Engineering
Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents
Haobin Li, Liang Jiang, Zhenyu Huang et al. (+2 more)
Recently, coding
agents have emerged as a dominant paradigm for real-world software engineering (SWE) scenarios, which solve complex tasks through multi-turn interactions with development environments. However, frequent interactions with environments would inevitably introduce substantial
token overhead, leading to high usage costs and latency. Although recent studies have explored reducing
token usage by context manipulation and interaction limits at inference time, these approaches focus on improving
token efficiency while overlooking the risk of discarding task-relevant information, thus struggling to balance the trade-off between resolution rate and
token efficiency. In this paper, we study a more general paradigm without suffering from the limitation, i.e., training
token-efficient coding
agents with promising resolution performance, which is a highly-practical yet less-explored problem. To this end, we reveal two core observations in SWE scenarios: i) Efficiency Variation: successful resolution could be achieved with fewer
tokens; ii)
Entropy Correlation: unproductive behaviors are associated with turn-level
entropy. Motivated by observations, we propose a novel reinforcement learning framework, dubbed HERO. Specifically, HERO prioritizes task resolution over
token efficiency during policy optimization and encourages efficient
reasoning patterns at both trajectory and turn levels. Extensive experiments on SWE-bench Verified and SWE-bench
Multilingual demonstrate that HERO achieves a favorable trade-off between resolution rate and
token efficiency compared with state-of-the-art coding
agents and reinforcement learning methods.
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All authors Haobin Li, Liang Jiang, Zhenyu Huang, Mouxing Yang, Xi Peng
Machine Learning
Right Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling
Zhong Li, Xin Huang, Jinhui Wan et al. (+6 more)
Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large
language models (LLMs) can automate this process, but the resulting correct formulations can require substantial time and
memory to construct and solve, limiting practical scalability. Therefore, we systematically investigate whether LLMs can identify problem structure from natural-
language descriptions and apply suitable optimization modeling techniques to generate mathematical models and solver code that solve the problems correctly and efficiently. To this end, we first curate OptTips, a knowledge base of 50 expert modeling techniques in eight families. Using this knowledge, we develop OptDachshund, a multi-
agent framework that transforms problems from existing optimization
benchmarks into new tasks for evaluating LLMs' use of modeling techniques. It constructs conventional and expert mathematical models with solver code for the same task and data, providing baselines for correctness and computational cost. The resulting EfficientOpt
benchmark contains 561 expert-reviewed tasks with paired reference implementations. Evaluation of 11 representative LLMs reveals an efficiency gap on correctly solved tasks with comparable measurements: for every LLM, most generated programs take longer to solve than their expert counterparts. Within the comparable reference-size subset, 57% of programs with correct objective values and fewer variables and linear constraints have longer recorded solver times. Case studies show that different modeling techniques can achieve the same optimal value at similar recorded cost. Faster solving may not reduce execution time if the code takes longer to prepare data and build the model. LLM optimization modeling should therefore be evaluated for both correctness and computational efficiency.
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All authors Zhong Li, Xin Huang, Jinhui Wan, Xiangyi Wang, Shenkai Zhang, Ruiqi Chen, Wenyu Liu, Zaiwen Wen, Ziyan Luo
Software Engineering
Toward Quantum Software Automation: A Quantum-Aware Harness for LLM-Guided Evolution
Lily Jiaxin Wan, Deming Chen, Klara Nahrstedt et al. (+1 more)
Quantum software is critical for improving the efficiency and reliability of scarce
quantum hardware. However, its design still relies heavily on ad-hoc, handcrafted heuristics that are often suboptimal and quickly become obsolete as
quantum hardware evolves. LLM-guided evolutionary search offers a promising way to automatically explore complex software designs, but existing search frameworks lack the
quantum-specific support needed for efficient
evolution: verification is expensive, feedback is sparse, and heterogeneous
quantum programs require different optimization objectives. In this paper, we present QSA, a
quantum-aware harness for LLM-guided evolutionary search toward automating
quantum software design. QSA equips the search with three forms of
quantum-specific guidance: an
evolution-hardness-guided coreset and approximate scoring to reduce verification cost, static and snapshot analyses to provide fine-grained execution context, and task-specific rewards for compiler passes and runtime policies. We evaluate QSA on the IBM
Quantum platform across three
benchmark suites. For multiprogramming, QSA improves QPU utilization by 4.2%-9.5% and Hellinger fidelity by 15.2%-19.5% over the state of the art. For error mitigation, QSA reduces mitigation time by at least 96.8% while achieving comparable or better fidelity. These gains require only $6.9 in LLM API cost over 11.3 hours.
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All authors Lily Jiaxin Wan, Deming Chen, Klara Nahrstedt, Bo Chen
Artificial Intelligence
STRATA: Self-Learning Through Role-Aligned Tiered Agents for Real-Time Strategy Games
Xinhe Tian, Xiaoyue Zhang, Ziyou Zhang et al. (+4 more)
Real-time strategy (RTS) games require
agents to coordinate economic development, production and construction, base defense, unit organization, and attack timing over long matches. Existing studies have applied large
language models to command decision-making in RTS games, enabling
agents to read textual game states and generate high-level plans. However, long inference latency can cause them to miss critical tactical events. The complexity and tactical diversity of full RTS matches also leave existing systems heavily dependent on manually written experience-based
prompts, with limited ability to learn continuously from past games. We present STRATA, a role-aligned hierarchical system with cross-game self-learning for Red Alert. STRATA assigns in-game strategic, logistical, and tactical decisions to a Strategic
Agent (SA), Logistics
Agent (LA), and Tactical
Agent (TA), respectively. The SA generates high-level directives based on the global game state and relevant experience cards, while the LA and TA handle logistics and tactical execution. After each match, a Review
Agent (RA) derives candidate experience from game traces, validates and revises it using evidence from subsequent matches, and compresses strategic experience supported across multiple games into concise experience cards for SA
retrieval. We evaluate STRATA through the formation of experience cards, full-match comparisons before and after learning, and experience learning against AI opponents with different play styles. Under a fixed scenario, using the learned experience cards increases the observed win rate from 30% to 100%. Sequential learning against AI opponents with different play styles also produces distinct long-term strategic experience.
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All authors Xinhe Tian, Xiaoyue Zhang, Ziyou Zhang, Jiacheng Li, Xiaoqiang Jin, Qianchuan Zhao, Gaochen Cui
Machine Learning · Cross-listed from stat.ML
Near-Minimax-Optimal Rates for Asynchronous TD Learning with Markovian Data
Yang Peng
We study the last iterate of standard importance-weighted temporal-difference (TD) learning from a single trajectory of a finite Markov decision process. The trajectory follows a behavior policy $b$ with an irreducible, possibly periodic state chain; a target policy $\pi$ covered by $b$ is evaluated with action importance ratios bounded by $\varrho$. Write $H=(1-\gamma)^{-1}$, with $\mu_{\min}$ the minimum stationary probability and $\mathfrak h_\star$ the largest expected hitting time of the reverse behavior state chain. We prove that the last iterate achieves sup-norm error at most $\varepsilon$ with high probability after $\widetilde O(\varrho H^3/(\mu_{\min}\varepsilon^2)+\mathfrak h_\star)$ transitions for $0<\varepsilon\le1$. The statistical term retains the cubic effective-horizon dependence of synchronous TD, while the initialization cost is additive in $\mathfrak h_\star$. Setting $b=\pi$ and $\varrho=1$ recovers the on-policy rate. For aperiodic behavior chains, $\mathfrak h_\star=\widetilde O(t_{\rm mix}/\mu_{\min})$ recovers a mixing-time bound. The proof combines an anchored local Poisson equation in reverse time to control fluctuations without a mixing-time factor, a compensation argument for successive hitting costs, and an expected cumulative propagation mass bound for decreasing step sizes. Given the policy ratios and their bound, an empirical state-count rule attains the same rate without prior knowledge of model parameters. The framework covers nonnegative affine importance-weighted prediction updates, including successor features, transition-dependent discounting, and coupled finite-mode predictions. A perturbation estimate yields a time-uniform guarantee for online return-variance estimation. A three-state, two-action construction gives matching minimax lower bounds for both terms up to logarithmic factors on the considered parameter classes.
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Machine Learning
Does Learning Protein Folding Generalize to Broader Reasoning?
Yong Liu, Zhanpeng Shi, Yizhou Dang et al. (+4 more)
Large
language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them.
Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can learning to fold
proteins teach general models reusable
reasoning capabilities? To answer this, we build FoldingCorpus, a
protein-derived question-answer
dataset, and Fold2Reason, a recipe that post-trains on it through two complementary signals: discrete structural answers predicted via the model's native
language head, and continuous 3D geometry decoded from the same shared representations. On FoldBench, Fold2Reason achieves structure prediction scores 2.7 to 3.5 times those of Qwen3.5-9B. Beyond
protein structure prediction, it improves performance on all 10
benchmarks spanning spatial, graph, scientific, and general
reasoning, raising macro-average accuracy from 45.09% to 48.33% (+3.23 pp), with positive gains on all 10
benchmarks, while matched controls built from random, synthetic, and shuffled structure yield substantially smaller or negative gains. Our work shows that non-linguistic, structure-dense scientific data can systematically improve broad
reasoning in
language models, making a solved scientific problem a practical source of post-training supervision.
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All authors Yong Liu, Zhanpeng Shi, Yizhou Dang, Zhongyue Zhang, Xiaoliang Shi, Zhijian Wei, Shuangjia Zheng