Cryptography and Security
Audience-Bound Persistent Memory: Authorization Across the Memory Lifecycle
Sibo Liu
A personal
language agent that acts for its owner across private and shared conversations can learn a fact from one audience and later place it in the context it assembles for another. We study authorization before context across the whole
memory lifecycle. Each
memory item carries the audience present when it was recorded; derived items are partitioned by audience, receive the intersection of their sources' audiences, or are suppressed; an audience widens only by an explicit, object-specific grant; and an item enters a model attempt only when every current viewer belongs to one of its authorized audiences, with unresolved viewers failing closed to public-only. Under explicit identity, provenance and complete-mediation assumptions, this admission is sound and policy-complete on the exact assembled context, enforced by exclusion rather than by model behavior. We realize it in two independently persisted reference architectures, a flat store and a relationship graph, and, descriptively, in a native
agent-
memory runtime. In a prospectively frozen confirmation over 10,000 multi-party histories, no forbidden item entered any architecture's context, whereas unscoped
retrieval exposed forbidden items in 82% of its contexts. Entitled recall matched policy-equivalent baselines exactly and exceeded unscoped
retrieval by 0.30 Recall@5, with a Holm-confirmed advantage that grows with distractors. No architecture produced a wrong-principal substitution, but unscoped substitutions were too rare to establish the prespecified joint decision.
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Cryptography and Security
TTMark: Pairwise Distortion-Free Watermarking Beyond Single-Token Entropy
Ruibo Chen, Zhengmian Hu, Donghang Lu et al. (+5 more)
Distortion-free watermarking enables reliable attribution of machine-generated text while preserving output distribution. However, existing methods operate independently on each generated
token, making their detection capability fundamentally constrained by the
entropy of the next-
token distribution. We present Tandem
Token WaterMark (TTMARK), a general pairwise watermarking framework that extends distortion-free watermarking from individual
tokens to adjacent
token pairs. By watermarking the joint distribution of consecutive
tokens, TTMARK enlarges the effective watermarking alphabet from V to $V^2$, allowing the detector to exploit both
token entropy and conditional
entropy while preserving distortion-freeness over the joint distribution. We further introduce a branch-isolating concatenated tandem generation
algorithm that efficiently constructs the joint distribution in a single forward pass. Theoretically, we show that pairwise watermarking achieves better expected detection strength in low-
entropy regimes. Extensive experiments across multiple
language models,
datasets, and three representative distortion-free watermarking schemes demonstrate that TTMARK consistently improves detectability without degrading generation quality, while also improving robustness to edits and substantially enhancing localized watermark detection.
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All authors Ruibo Chen, Zhengmian Hu, Donghang Lu, Xuehao Cui, Georgios Milis, Yihan Wu, Jian Du, Heng Huang
Software Engineering
LatentSift: Policy-State Filtering for Token-Efficient Verification of Software Engineering Agents
Yuning Han, Yangchenchen Jin, Tyler Jandreau et al. (+1 more)
Test-time scaling improves software engineering
agents by generating multiple candidate trajectories and selecting the best one. Verifying and selecting among these long interactions can consume as many
tokens as generation itself. Existing hybrid workflows first apply an LLM-based execution-free (EF) verifier to filter candidates before running tests, which adds another model pass over every trajectory. We introduce LatentSift, a
token-free and execution-free filter that replaces this first stage with hidden states the policy already produces while generating the candidates. It represents each candidate through its
reasoning, observation, and function-call states, compares them with positive and negative banks of such states collected from successful and unsuccessful trajectories during policy training, and fuses the resulting distance scores with a learned linear score to retain promising candidates for the execution-based stages. On SWE-bench Verified, across three
agents and two policy sizes, LatentSift cuts EF-verifier
tokens by 66.6–81.0% and total verification
tokens, which include test generation, by 49.1–62.1% at K=16, while hybrid Best@16 matches or improves on each
agent's reference workflow, rising from 59.26% to 60.06% on DeepSWE-Preview.
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All authors Yuning Han, Yangchenchen Jin, Tyler Jandreau, Jingwei Sun
Machine Learning
AdaKerNet: Neural Kernel Decoding for Task-Adaptive Prediction with Multimodal Large Models
Konstantinos D. Polyzos, Eleni Oikonomou and Tara Javidi
Large foundation models have been introduced with the promise of efficient adaptation to downstream tasks. Yet, under limited supervision, MLLMs, an important class of large foundation models, remain challenging to adapt to various downstream tasks. Adaptation typically relies either on MLLM parameter fine-tuning or on training neural-based decoders. Both approaches struggle under limited supervision, while fine-tuning additionally requires access to model parameters, which is often unavailable for closed-source models. We introduce AdaKerNet, a novel learnable task-adaptive neural kernel decoder. AdaKerNet is fully agnostic to the parameters of the underlying MLLM and operates solely on its (frozen) rich representations obtained from the diverse available modalities. AdaKerNet relies on (i) a set of learnable, Lipschitz-controlled
multimodal features derived from these MLLM representations; (ii) a reference kernel that provides a soft structural prior on those features; and (iii) a lightweight nonlinear neural predictor that adaptively deforms that structure. Learning the kernel representation and the neural predictor jointly within a unified optimization framework allows AdaKerNet to capture features and
geometric relationships relevant to the downstream task. Numerical tests across four MLLMs: BLIP-2, LLaVA-1.5, Qwen2.5-VL, and Gemini
Embedding 2, and
multimodal inputs spanning text, audio, images, and tabular measurements demonstrate significant and consistent improvements over direct MLP,
attention-, autoencoder- and kernel-based decoders, across a range of scarce-label budgets, with average error reduction of up to 41% across baselines. These results establish AdaKerNet as an effective approach for prediction from frozen
multimodal representations in the scarce label regime. Additional structural ablations highlight the complementary contributions of AdaKerNet's components.
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Artificial Intelligence · Cross-listed from q-bio.NC
Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex
Iishaan Inabathini and Margaret M. Henderson
Understanding how the
brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used deep neural network (DNN) models to build stimulus-computable fMRI encoding models that predict single-voxel responses to complex natural
videos. However, the majority of
video-computable encoding models predict responses using simple linear mappings from model
tokens, overlooking the spatiotemporal structure shared by
video representations and neural responses. Recent cross-
attention encoding models address this limitation for static images, enabling flexible stimulus-dependent weighting of image content across space. Here, we extend this framework to naturalistic
video, using per-parcel cross-
attention to dynamically route features from a self-supervised
video model (V-JEPA-2) across both space and time, fitting this model to fMRI responses to short
video clips. We compare joint spatiotemporal
attention with factorized and selectively constrained alternatives, and find that joint routing improves predictions of
brain responses to held-out
videos across higher visual regions, most consistently in lateral and dorsal visual areas associated with dynamic motion perception. Moreover, our method provides interpretable, stimulus-specific
attention maps that dynamically follow moving objects, revealing which locations and temporal moments contribute to each neural response. We further show that
attention maps from parcels in different category-selective networks (face-, body-, scene-selective) differentially weight content in accordance with expected semantic selectivity. Together, this work provides a new computational framework for understanding how visual information is adaptively weighted by cortical populations during dynamic visual perception.
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Artificial Intelligence
Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents
Kehang Zhu, Anand Shah and David Parkes
Can interaction formats and textual scaffolds help large
language model (LLM)
agents make better decisions, and do better decisions come with better explanations? We study these questions in auctions and matching, multi-
agent environments with explicit rules and known optimal strategies. These settings let us vary how a decision problem is presented while retaining a
benchmark for evaluating behavior. Drawing on human-motivated theories of simplicity, we compare interfaces that elicit a complete bid or ranking with sequential interfaces that make safe choices easier to identify. We then hold the interaction format fixed and vary
reasoning scaffolds and rule descriptions. Across four model families, the ascending auction interface substantially reduces bid deviations. The matching comparison also shows why sequential responses require different error accounting from complete rankings. Laying out payoff contingencies and explaining why truth-telling is safe also improve choices, whereas
prompts to plan through matching rounds or form beliefs about opponents worsen play overall. In auctions, these behavioral gains are not accompanied by corresponding improvements in measured verbal indicators of strategic understanding in the
agents' short stated plans. Other
prompts change those indicators without improving bids. Our findings suggest that human-motivated theories of simplicity can inform the design of decision environments for artificial
agents. They also show why scaffolds should be evaluated through realized choices as well as explanations: improvements in one need not appear in the other.
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Computer Vision
Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation
Xiaoyu Wu, Weihang Guo, Yifei Wang et al. (+3 more)
Standard
video generators do not natively compact historical context into reusable
memory tokens. As generation continues, the growing history makes it increasingly difficult to retain information from earlier frames due to long-context degradation. Key-frame-based approaches address this challenge by retaining selected past frames, but can discard information needed for future generation. Rather than relying on frame selection alone, we study whether a frozen
video generator can supply the supervision needed to learn a compact representation of the history. We propose Prediction-Aligned Context Compaction (PACC), which uses a learned compressor to aggregate information across past frames into compact
memory tokens. We train the compressor through on-policy
distillation, using the same frozen generator both as a student when conditioned on compressed
memory and as a teacher when conditioned on the full history. The student generates continuations, while the teacher provides targets for the same noisy inputs at each denoising step. Only the compressor is updated to align the student's predictions with these targets. We evaluate PACC on MBench, which jointly measures
memory-event coverage and consistency. PACC outperforms the strongest baseline by 6.63 points on Causal-rCM and 3.19 points on Causal Forcing. Evaluation on VBench-Long using MovieGen
prompts further shows that PACC produces minute-long
videos with generation quality competitive with baselines. Together, these results show that learning to compact historical context can improve long-
video memory without modifying the underlying generator.
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All authors Xiaoyu Wu, Weihang Guo, Yifei Wang, Xinze Feng, Lydia E. Kavraki, Zhiwei Steven Wu
Software Engineering
Strategies for Deploying AI Agents in Production at Scientific User Facilities
Ming Du, Xiangyu Yin, Michael Prince et al. (+40 more)
Agentic artificial intelligence (AI) is moving beyond research demonstrations toward production use at scientific user facilities, including light sources,
neutron sources, nanoscience centers, and autonomous laboratories. Its scientific value extends beyond increasing throughput.
Agents can perform repeatable tasks in calibration, measurement execution, and quality control, as well as initial analyses that turn data into reviewable evidence, allowing scientists to focus on hypotheses, unexpected observations, and interpretation. Drawing on deployments of LLM-driven
agents at the APS, this perspective distills practical strategies with an emphasis on elements that can be reused across instruments and facilities. We discuss
agent harnesses for beamline control, facility knowledge
retrieval, and data analysis while keeping the underlying design principles independent of any specific implementation. These principles cover inference endpoints, tool-server architectures, non-text data, computationally intensive services, reusable skills, and governed learning throughout an instrument's lifecycle. We also consider how network and Linux operations, governed shared
memory, and deterministic orchestration can extend these patterns across facility services. Because LLM capabilities continue to evolve, these recommendations represent a snapshot of the technology as of the date on the cover.
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All authors Ming Du, Xiangyu Yin, Michael Prince, Yi Jiang, Rajat Sainju, Tekin Bicer, Yanqi Luo, Eric Codrea, Peco Myint, Nina Andrejevic, Juanjuan Huang, Trupti Mohanty, Pawan Tripathi, Dishant Beniwal, Hemant Sharma, Doga Gursoy, Aileen Luo, Tao Zhou, Chenran Xu, Jan Ilavsky, Matthew T. Dearing, Ryan Chard, Hoon Seo, Dariusz Jarosz, Elaine Chandler, Thomas Fors, Hairong Shang, Pete Jemian, Xuli Wu, Madeline Miller, Ryan Aydelott, Nicholas Schwarz, Francesco de Carlo, Byeongdu Lee, Yine Sun, Moises Smart, Philippe Piot, Joe Sullivan, Mathew J. Cherukara, Alec Sandy, Stefan Vogt, Jonathan Lang, Laurent C. Chapon
Data Structures and Algorithms · Cross-listed from math.PR
Distance flexibility in spatial matching: the value of concentration
Taha Ameen and Sophie H. Yu
In spatial matching
markets, a supply unit's flexibility is measured by its service radius, the maximum distance at which it can serve demand. In dimensions $k \geq 2$, we study how a platform should allocate service radii among the supply nodes subject to a budget on their sum. The platform makes this choice before observing supply and demand locations, with the objective of maximizing the expected fulfilled demand. We show that the shape of a preferred allocation depends on the total budget: under suitable conditions, large budgets favor allocations that are more uniform in the sense of majorization, while small budgets favor concentration. We also characterize a non-uniform allocation that is asymptotically optimal for a very-sparse regime, and show that the uniform allocation is suboptimal in this regime. Our results provide theoretical explanations for the radius allocation questions raised by the numerical experiments in [ASY26b].
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Artificial Intelligence
Better Nearest Neighbor Graph Indices via (Efficient) LLM-Guided Pruning
Fangzhou Wu, Haike Xu and Sandeep Silwal
Graph-based approximate nearest neighbor search (ANNS) is widely used for large-scale semantic search. Its indices are constructed primarily based on
geometric relationships among
embeddings of an input
dataset (e.g., documents or images), rather than explicitly optimizing for semantic relevance. However, when using these indices for downstream query
retrieval, performance is evaluated based on the semantic relevance of the retrieved results to the query. This creates a fundamental "geometry-semantic" mismatch between how the indices are constructed and how their
retrieval results are evaluated. While existing LLM-based reranking methods can partially mitigate this mismatch at query time, they leave this underlying structural problem in the graph unresolved. We therefore propose LLM-Guided Graph
Pruning (LGP), a general framework that addresses this mismatch directly by leveraging LLM
reasoning to refine an existing ANN graph index itself. LGP identifies structurally "low-value" neighbors of nodes and replaces them with LLM-selected alternatives that provide useful semantic information while retaining desired
geometric structures of the original graph, including sparsity and efficient navigability. Experiments on representative semantic
retrieval benchmarks show that LGP consistently improves end-to-end
retrieval performance over both vanilla greedy graph search and LLM-based reranking across widely used graph-based ANN indices such as DiskANN and HNSW.
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Artificial Intelligence
HyperZip: Efficient Data Compression through Personalized Diffusion LLMs with Hypernetworks
Thai Nguyen, Khang Tran and NhatHai Phan
Large
language models (LLMs) have shown strong potential for lossless data compression, but existing approaches are constrained by the high computational cost and low throughput of autoregressive decoding. We propose HyperZip, an efficient and scalable LLM-based compression framework that leverages
diffusion-based LLMs (dLLMs) with Multi-
Token Prediction (MTP) to accelerate LLM-based data compression processes. We identify a trade-off in
diffusion-based compression, where increasing decoding throughput degrades the compression rate. To mitigate this trade-off, HyperZip employs a hypernetwork to generate data-specific updates from a context representation, adapting the dLLM to the target data without costly fine-tuning, resulting in a low compression rate and high throughput. Extensive experiments show that HyperZip achieves a superior trade-off between compression rate and speed compared with state-of-the-art baselines.
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Machine Learning
Explainability from Training with Applications to TCR-Epitope Prediction
Jiarui Li, Zixiang Yin, Samuel Landry et al. (+2 more)
Deep learning models have achieved strong performance in artificial intelligence for science, yet their black-box nature limits our understanding of how they learn scientific tasks. Existing methods for interpretability provide limited insight into how models organize evidence and evolve during learning. We introduce explainability from training (EFT), a model-agnostic paradigm that traces model interpretation during training to explain why models rely on specific features and how they organize these features as predictive evidence. We apply EFT to four state-of-the-art T
cell receptor (TCR)-epitope prediction models, TCR-SRIM, TULIP, MixTCRpred, and NetTCR-2.2, spanning post-hoc and interpret-by-design approaches as well as
transformers and CNNs. To investigate how structural information affects model explanations, we introduce a
benchmark, TCR-XAI2, containing 388 unique experimentally resolved TCR-epitope structures, complemented by structures predicted using AlphaFold3, Boltz-2, TCRModel2, tFold-TCR, and OpenFold3. Using EFT with TCR-XAI2, we demonstrate that (1) CNN and
transformer models exhibit distinct learning trajectories; (2) TCR $\alpha$ and $\beta$ evidence can conflict during learning, limiting the benefits of jointly modeling both chains, while MHC information mitigates this; and (3) real versus predicted structural data for TCR-epitope prediction exhibits distinct TCR and peptide feature preferences as well as differing trajectories of model certainty.
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All authors Jiarui Li, Zixiang Yin, Samuel Landry, Zhengming Ding, Ramgopal Mettu
Computer Vision
StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks
Ziyi Yin, Sangmin Woo, Kang Zhou et al. (+4 more)
Vision-
language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from a single command. Online reinforcement learning (RL) can improve these policies through environment interaction, yet many existing methods provide reward only after the complete task succeeds. However, such terminal supervision is sparse and does not distinguish early failures from rollouts that make substantial partial progress. We propose StructRL, an online RL framework that constructs structured intermediate supervision from verifiable subtask completions. StructRL decomposes each task into verifiable subtasks, grants intermediate rewards only after the prerequisite subtasks have been completed, and scales each reward according to completion pace. Across RoboCasa365 and LIBERO-Long with GR00T-N1.5 and pi 0.5, StructRL consistently outperforms evaluated online RL baselines. These results show that verifiable, structured intermediate rewards improve long-horizon VLA post-training. Code is available at https://github.com/amazon-science/StructRL.
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All authors Ziyi Yin, Sangmin Woo, Kang Zhou, Sungyeon Kim, Aosong Feng, Haibo Ding, Jun Huan
Sound
Trigger Sound Suppression for Misophonia
Vaishnavi Vidyasagar, Jasmine Zhang, Mahima Uliyar et al. (+4 more)
Misophonia, a disorder of decreased tolerance to specific sounds, affects 5-20% of the population, yet sufferers have no good options: therapy helps a minority, and earplugs or noise cancellation silence everything. We present a study for neural trigger sound suppression for misophonia, selectively removing trigger sounds. We curate a
dataset covering the 10 most common trigger classes. Using streaming dual-path networks operating on 6 ms audio chunks, we explore both one-hot and multi-hot-conditioned models that suppress 1-3 triggers from the acoustic scene. We validate our model outputs in a listening study with 30 adults with clinically elevated misophonia impairment. Participants reported significantly lower distress and arousal, and improved valence, for suppressed audio.
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All authors Vaishnavi Vidyasagar, Jasmine Zhang, Mahima Uliyar, Seunghyun Oh, Emily Catherine Gates, Mark Zachary Rosenthal, Shyamnath Gollakota
Computer Vision
Xiaoyu Wu, Yifei Wang and Chen Wei
Generative and representation learning remain asymmetrically connected: semantic representations are used to improve
diffusion generation, whereas the models' own representations are often treated as a by-product of synthesis. We ask whether
diffusion models can instead be trained to learn substantially stronger semantic representations without sacrificing generation quality. SelfFlow takes a step in this direction by introducing self-supervised patch
alignment into flow matching, but its main gains remain in faster convergence and improved generation. Inspired by DINO and iBOT, we extend this framework with cross-view class-
token alignment to further strengthen semantic representations. Specifically, we form two independently noised, dual-timestep observations of each image and align each student class-
token representation with the stop-gradient EMA-teacher target from the other observation. This objective is optimized jointly with the inherited flow-matching and local patch objectives. Notably, although the additional objective acts only on the class
token, it strengthens both class-
token and patch representations. Compared with a matched two-view baseline, ImageNet linear-probing accuracy improves by 9.4% using the class
token and 10.1% using mean-pooled patch
tokens, while frozen-backbone VOC2012
segmentation improves by 3.6 mIoU. These representation gains are achieved while maintaining comparable ImageNet generation FID. In text-to-image training, the same objective also improves generation FID, reducing it from 2.52 to 2.37 at matched checkpoints. Our results show that representation need not remain a by-product of generation or merely a tool for improving it: it can be directly optimized as a first-class capability of
diffusion pretraining alongside generation.
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Neural and Evolutionary Computing
Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip
Eric Oliveira-Gomes and Damien Rontani
Hardware accelerators based on physical dynamical systems offer an attractive route toward energy-efficient reinforcement learning applications. However, their scalability is challenging because it requires many statistically independent
entropy sources. Here, we introduce a quasi-analog decision-making architecture based on asynchronous Boolean networks (or
lattices) implemented on a clockless reconfigurable chip. Each node in the network consists of a single logic element that acts as an autonomous
entropy source. This architecture gives rise to distributed Boolean chaos, in which a spatially coupled network generates parallel streams of chaotic Boolean transitions with very low statistical dependence between nodes. We experimentally demonstrate parallel decision-making on a 1024-armed bandit problem, which is beyond the scale of previous hardware implementations, while significantly improving power-law scaling performance. Separately, we scale the proposed
entropy source to 5120 parallel channels, yielding an aggregate sample generation rate of 2.14 TS/s. Our solution is implemented on a commercial reconfigurable CMOS chip and offers high integration density and ease of programmability. Our results pave the way for using distributed Boolean chaos as a valuable hardware substrate for large-scale reinforcement learning and for the development of fully integrated, high-throughput decision-making accelerators.
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Game Theory
Solving the Arctic Product-Mix Auction
Elizabeth Baldwin, Paul Klemperer and Edwin Lock
Arctic product-mix auctions allow budget-constrained bidders to express preferences over multiple substitute goods. We give the first strongly
polynomial algorithm to find the competitive
equilibria when the auctioneer has separable convex piecewise-linear costs. It combines a strongly
polynomial reduction to the costless Arctic auction with an existing strongly
polynomial algorithm for costless Arctic auctions. We also give a
polynomial-time Turing reduction from costless Arctic auctions to linear Fisher
markets, and a strongly
polynomial many-to-one reduction to linear Arrow-Debreu exchange
markets. If a good is sold in positive quantity in two competitive
equilibria, its price is the same in both.
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Computation and Language
DeepRewind: Predicting and Repairing Premature Commitments in Deep Research Agents
Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Lele Wang et al. (+2 more)
Deep-research
agents conduct long-horizon investigations through iterative search, evidence evaluation, belief revision, and synthesis. However, they may commit to claims before sufficient evidence is available, causing later
reasoning to reinforce an incorrect interpretation. We introduce DeepRewind, an additive control layer for reversible deep research that represents the
agent's evolving epistemic state as a typed graph of sources, evidence, claims, hypotheses, assumptions, commitments, plans, and drafts. Before accepting an intermediate conclusion, a
prompt-based world model predicts its impact and estimates reversibility based on hypothesis narrowing, information loss, recovery cost, and contradiction-trigger coverage. A binary controller blocks risky commitments, while a consistency monitor performs dependency-aware rollback when later evidence invalidates them. Across DRBench and LiveDRBench, DeepRewind improves insight recall by 3.6 percentage points and reduces premature commitments by 59.1% relative to Open Deep Research.
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All authors Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Lele Wang, Peter West, Giuseppe Carenini
Artificial Intelligence
ThuRunel: Dynamic Decoupling for Structured Advisory Dialogue
Yuyan Chen
High-stakes advisory domains such as medical aesthetics, legal consultation, and educational planning exhibit a two-phase structure. The early phase requires empathetic elicitation and emotional support, and the late phase requires authoritative specialist judgment. Neither fully automated
agents nor human junior consultants adequately address this structure at scale. We formalize the core design challenge as dynamic decoupling, asking how an AI advisory
agent should decide what to ask, when to stop, what to resolve autonomously, and what to forward to the specialist. We present ThuRunel, an advisory
agent combining a finite-state belief management framework, a chain-of-thought teacher synthesis protocol, and learned generation adapters. Against eleven baselines, ThuRunel achieves consistent improvements in elicitation completeness and specialist brief quality. ThuRunel is publicly deployed as a bilingual web application in which the same decoupling decisions operate from the client's side, grounded in a curated knowledge base that cites its sources in every answer.
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Machine Learning
Adapting Linear-Time Architectures for Tabular In-Context Learning
David Schnurr, Felix Sarnthein, Thomas Hofmann et al. (+1 more)
Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax
attention limits their use on large
datasets. Existing linear-time alternatives, however, are mostly causal, and their potential for tabular in-context learning (ICL) remains underexplored. To address this, we (1) revisit causal training setups, (2) compare linear sequence mixers, and (3) investigate their ICL generalisation beyond the pretraining context length. First, we show that the best training setup for causal models resembles next-
token prediction. Then, perhaps surprisingly, the most promising linear sequence mixer is causal: DeltaNet outperforms even non-causal linear
attention. However, it degrades beyond $2$-$4\times$ the pretraining context length, and existing mitigation strategies such as bidirectionality defer the problem at best. A hidden-state oracle shows that this is not a capacity problem. Instead, our analysis points to an instability in the recurrent state, which drifts in deeper layers of causal models. Since DeltaNet's learned write rates overfit to the pretraining regime, we modulate them with a time-dependent decay schedule intervention to stabilise length generalisation. Finally, re-introducing non-causality by reading out from the final state allows us to closely match a controlled softmax
attention baseline on OpenML-CC18 and TabArena.
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All authors David Schnurr, Felix Sarnthein, Thomas Hofmann, Imanol Schlag
Cryptography and Security · Cross-listed from quant-ph
Conversable Quantum Fire in the Standard Model
Ofer Casper, Barak Nehoran and Or Sattath
Quantum fire consists of
quantum states that can be efficiently functionally cloned but cannot be transmitted using one-way classical communication. Whereas all previous
quantum-fire constructions with proven untelegraphability are relative to an oracle, ours, based on one-shot signatures, is in the standard model. Our construction achieves two further notions that we introduce: (a) keyless untelegraphability, a strengthening of untelegraphability; and (b) conversability, meaning that a flame, though not telegraphable via one-way classical communication, can be transmitted via classical interaction. Hence one-way and two-way classical communication differ qualitatively in their power to transmit this
quantum fire. We use these two novel properties of
quantum fire, along with its clonability, to give one of the first cryptographic applications of
quantum fire. Keyless untelegraphability forces the distribution of serial numbers of valid flames to have high min-
entropy, while conversability and clonability allow the corresponding flame to be cloned and transferred using only classical communication. We present two variants of our conversable
quantum-fire construction. The first, based on one-shot signatures (instantiable from subexponential iO, subexponentially secure one-way functions, and LWE), supports polynomially many flames, and super-logarithmic min-
entropy of the serial-number distribution. The second, under the stronger assumption of exponentially unforgeable one-shot signatures (instantiable relative to a classical oracle), supports exponentially many flames, and linear min-
entropy.
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Robotics
ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning
Ke Fang, Yupu Yao and Lu Cheng
Latent world models rely on representation geometry for planning, yet regularizing the latent marginal alone does not determine the state-to-state relationships used for action selection. We show that this can cause planning-relevant novelty structure to be weakened as representations are transformed into the final latent used by the planner. We introduce Aligned Transport of Latent Structure (ATLAS), a training objective that explicitly preserves relational geometry while calibrating the global latent distribution. ATLAS transfers normalized pairwise structure from an informative encoder representation to the planning latent and uses Wasserstein
embedding matching (WEMReg) to calibrate its marginal through one-dimensional Wasserstein-2 transport. Our analysis shows that relational preservation and marginal calibration impose non-redundant constraints, and connects finite-candidate planning stability to relational distortion, latent-scale mismatch, and prediction error. Instantiated in LeWM, ATLAS improves mean goal-reaching success across PushT, TwoRoom, and OGBench-Cube on both lower- and higher-novelty evaluation subsets, with the largest gain on higher-novelty TwoRoom episodes. Representation and rollout diagnostics further show stronger novelty-related structure in the planning latent, improved marginal calibration, and lower multi-step prediction error. Together, these results highlight preservation of planning-relevant latent geometry as an important ingredient for reliable world-model planning. Code is available at https://anonymous.4open.science/r/atlas-world-model-72C4/.
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Cryptography and Security
Calibrating One-Round Membership Inference with Neighbors
Francesco Rita, Jie Zhang and Florian Tramèr
The state-of-the-art Membership Inference (MI) methods calibrate their signal separately for each example using reference models, auxiliary models trained to exclude the target. This paradigm scales poorly to modern large models, however, whose training is too expensive to replicate. This has motivated one-round settings, where only a single trained model is available; but without reference models the per-example calibration that drives the strongest attacks can no longer be estimated, leaving the membership signal weak. We ask whether neighbors of the target point can recover this calibration without training any additional model. Our key observation is that reference models serve only to reveal how an example behaves under models not trained on it, and that querying the target model on nearby samples yields the same information. We propose two complementary ways to obtain such neighbors, and show that querying them against an early training checkpoint further sharpens the signal. We evaluate across three image classification
datasets and three training setups, showing that neighbors yield strong membership signals and competitive attack performance at no additional training cost.
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Cryptography and Security
DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning
Pedro Beltrán-López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli et al. (+2 more)
Decentralized
Federated Learning (DFL) eliminates the central aggregation server, reducing the single point of observation that traditional defenses against attacks rely on. As a result, peer-to-peer networks become exposed to malicious updates containing
backdoors or semantic poisoning, since such updates can remain close to benign ones in the parameter space while behaving very differently. This may evade defenses based on passive parameter inspection. However, existing deception-based defenses have mainly been designed for centralized FL and do not jointly address local observation, poisoning propagation, source attribution, and containment in strictly serverless DFL. To address these limitations, this paper presents DecoyTrace, a proactive cyber deception-based defense for strictly serverless DFL environments. DecoyTrace deploys a mobile DecoyNode that generates decoy challenges using chaotic maps, disseminates a dual model (clean vs. decoy) based on neighbor trust, and evaluates them using three-state semantic metrics. Upon confirmation, a distributed protocol isolates the source and performs a model reset or recovery to preserve training progress. Evaluated across sixty configurations on the NEBULA platform (five
datasets, three
topologies, and four attack/defense scenarios), DecoyTrace systematically restores lost utility. The F1-score remains within 0.03 of the baseline on MNIST/FashionMNIST (mitigating drops of up to 0.37), matches or exceeds the baseline on EMNIST and CIFAR-100, and remains between 0.05 and 0.10 below the baseline on CIFAR-10, the most visually complex convolutional scenario evaluated. Furthermore, containment reduces CPU and network usage by up to two-thirds. These results demonstrate the feasibility of unifying deception, identification, and containment in DFL, while also identifying its limitations in complex tasks and multi-attractor threat models.
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All authors Pedro Beltrán-López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli, Manuel Gil Pérez, Alberto Huertas Celdrán
Machine Learning
Reducing the Adaptation Gap Through Reachable Fisher Geometry
Wasif Jalal, Sachin Deb and Asif Salekin
Parameter-efficient fine-tuning (PEFT) determines not only how many parameters are trained, but also which local directions a model can move in, so similar adapters can affect subgroup losses differently. Since
curvature matrices are infeasible to form at adapter scale, scalar summaries such as the Fisher trace are often used instead. We study what the trace reveals and what it loses through the reachable Fisher: each subgroup's full-model Fisher pulled back through the adapter Jacobian. Under likelihood losses, it represents the Gauss-Newton
curvature accessible to the adapter, and its trace can be computed from score-gradient norms without forming the full
matrix. Under matched subgroup gradients, a positive-definite reachable-Fisher difference, with a margin exceeding the Hessian-Fisher defect, implies that every sufficiently small nonzero model-changing update increases the signed gap. In contrast, the restricted operator norm determines worst-case quadratic change, while a
matrix-free Frobenius discrepancy bounds its reachable-Fisher component. Trace alone cannot certify definiteness or control
matrix mismatch. Equal traces rule out a positive-definite difference but can still hide large operator discrepancies. Across 306 single-seed models, higher trace accompanies greater subgroup difficulty in 75.7 percent of 1,218 eligible evaluations, while trace matching reduces the best-worst subgroup gap in all 30
dataset-encoder-adapter combinations. However, held-out audits show that operator discrepancy decreases in 23 of 30 combinations, while the unbiased squared-Frobenius statistic decreases in only 16 of 30. Fisher trace is therefore a scalable diagnostic and training heuristic, but not a certificate of local gap behavior or
matrix alignment.
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Artificial Intelligence
PILLAR: Private Inverted-Index Lexical Lookup for Augmented Retrieval
Truong Son Nguyen, Daniel Blackley, Ni Trieu et al. (+1 more)
Retrieval-augmented generation (RAG) hands the user's query to whoever hosts the corpus. We propose PILLAR, a
Privacy-Preserving RAG (PPRAG) system based on Private Information
Retrieval (PIR) in which a client utilizes the k documents most similar to their query from a server-held and publicly known corpus to respond to their query, while the server learns nothing about the query, either its terms or its access pattern. Prior PPRAG constructions rely on dense
retrieval alone, translating approximate nearest-neighbor search into many query-dependent rounds of PIR, and pay for it in both latency and
retrieval quality. PILLAR instead performs private hybrid
retrieval in two stages. A sparse stage issues a small, fixed number of PIR queries against a carefully designed index of precomputed BM25 scores, filtering the corpus down to candidates that share terms with the query without the server ever seeing which terms these are. A dense stage then fetches only those candidates' document
embeddings and re-ranks them locally, avoiding the many costly PIR queries that private dense
retrieval typically requires. We instantiate PILLAR with two protocols that trade latency against
retrieval quality, each built on a different private rendering of lexical search. PILLAR-Bin bins posting lists into a hash table and is a single-round design that achieves lower latency than state-of-the-art private
retrieval schemes. PILLAR-Tree turns block-max
pruning into an oblivious tree traversal combined with cuckoo hash tables and achieves the highest
retrieval quality at lower latency than state-of-the-art schemes.
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All authors Truong Son Nguyen, Daniel Blackley, Ni Trieu, Evgenios M. Kornaropoulos
Sound
Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech
Yentl Collin, Evan Dufraisse, Amr Mohamed et al. (+3 more)
Flow-matching text-to-
speech (TTS) models achieve high synthesis quality but require many neural function evaluations (NFEs) to integrate their generative trajectories. Recent few-step flow-map
distillation approaches for TTS construct targets from numerically integrated teacher trajectories, creating a trade-off between target accuracy and training cost. We propose Local Flow-Map
Distillation (LFMD), which adapts Eulerian Map
Distillation to conditional TTS and avoids teacher trajectory integration during target construction. For inference, we derive a sampling schedule (TD-DP) from teacher dynamics and consistency of the learned maps, with a single cost graph supporting multiple NFE budgets without external audio-metric evaluation. Because scheduling offers no flexibility at one NFE, we refine this regime with
alignment-aware temporal self-
distillation using soft-DTW. Across Seed-TTS and LibriSpeech-PC, LFMD improves low-NFE synthesis over a matched integral-
distillation baseline. On Seed-TTS, the refined student reaches 1.80% WER with 1-NFE, compared with 1.76% for its 32-NFE teacher.
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All authors Yentl Collin, Evan Dufraisse, Amr Mohamed, Amine Khelif Khelif, Dani Bouch, Guokan Shang
Artificial Intelligence
Towards an AI Software Factory for Data Systems
Anna Pavlenko, Bogdan Crivat, Brandon Haynes et al. (+23 more)
AI-assisted coding tools deliver significant acceleration of coding, but only limited impact across the end-to-end software development lifecycle (SDLC)–an Amdahl's law effect! In this paper, we discuss our progress towards building an AI SW Factory that accelerates all the stages of SDLC-Targeting, Coding, Reviewing, and Ops. The AI SW Factory produces a metadata exhaust that enables self-improvement by fine-tuning model weights and updating our World Model (a rich data substrate). We focus on Data Systems and the important class of Evolutionary Coding Tasks (i.e., those with a measurable objective to hill-climb) and report on 1) scaled deployments at Microsoft (tens of repositories) leading to 3x engineering efficiency above agentic coding and up to 22x
token efficiency, and 2) several open challenges.
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All authors Anna Pavlenko, Bogdan Crivat, Brandon Haynes, Carlo Curino, Fotis Psallidas, Jaro Slawinski, Johannes Freischuetz, Laura Pereira Sanchez, Markus Weimer, Mathieu Demarne, Matthias Jasny, Mauktik Gandhi, Max Bovykin, Mirco Milletari, Purbasha Ghosh, Qiushi Bai, Raghu Ramakrishnan, Rahul Pandita, Sergiy Matusevich, Shivaram Venkataraman, Subru Krishnan, Md. Tareq Mahmood, Tiemo Bang, Venkatesh Emani, Xuan Zhao, Yiwen Zhu
Machine Learning
Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression
Xingyu Zhu, Pu Yi, Ziheng Cheng et al. (+5 more)
Chunked KV-cache compression reduces the
memory and
attention costs of long-context inference by compressing windows of consecutive
tokens into fewer cache entries at a fixed stride. Such compression also introduces a new positional coordinate: a
token's phase, or its position relative to compression-window boundaries. We uncover a systematic asymmetry in models using such compression: the same information can be easy to retrieve at one phase and difficult at another. We call this periodic variation in
retrieval performance phase sensitivity. In large open-weight models with such compression, long-context
retrieval accuracy can differ by up to 40 percentage points across phases, revealing periodic weak spots that average
benchmark scores can conceal. To investigate this behavior, we pretrain a family of
transformers from scratch across multiple KV-compression designs, reproducing phase sensitivity across the variants. Mechanistic analysis using causal interventions in these models reveals phase specialization: different
attention components contribute asymmetrically to retrieving information at different source phases. We further analyze idealized
retrieval models, showing how gradient flow dynamics may favor sharp phase specialization. Evaluating models with chunked KV-cache compression thus requires measuring across compression phases: high average accuracy can coexist with systematic positional failures.
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All authors Xingyu Zhu, Pu Yi, Ziheng Cheng, Ang Lv, Jing Liu, Lexing Ying, Yiyuan Ma, Xin Dong
Artificial Intelligence
StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents
Ziyang Yu, Liang Zhao, Bowen Zhu et al. (+1 more)
Despite the recent success of coding
agents built on large
language models, it remains challenging to run them over long horizons, since every observation is appended to the context and the context grows with each one. History-based maintenance is a common remedy, which masks or summarizes old observations, or prunes what a model reads as useless, and bounds the context at little cost. However, it decides from the text of the history alone and sees nothing of how the code is connected. Since a coding
agent edits code many times over a single task, and each write can change what code elsewhere means, such maintenance may keep records a write has falsified, drop ones that still hold, and miss code the
agent needs next. To overcome these challenges, this paper proposes StateTape, a novel and scalable framework that rewrites a coding
agent's context as the repository changes rather than as the context grows. The key idea of StateTape is to model the repository as a symbol-level code graph, whose dependencies and
language rules expose which symbols a write can affect. Upon this graph, a tape marks the symbols each write changed, which turns staleness from an inference about text into an observation of the
agent's writes. We propose a per-write procedure in which the tape nominates the records a write could have falsified while a small manager model settles what the write log cannot, and further provide a theoretical analysis and TraceBench, a
benchmark that labels what an
agent is holding against what is actually needed. Empirically, we demonstrate that StateTape can effectively clear falsified records and retrieve what is needed, and thus achieve a higher resolve rate in all experiments spanned by six coding
agents and three edit-heavy
benchmarks with little computational overhead.
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All authors Ziyang Yu, Liang Zhao, Bowen Zhu, Hasibul Haque