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新论文 · v1arXiv:2611.16590

AstroMemBench: A Benchmark for Memory Systems in Embodied Spacecraft Agents

You Wu, Guhong Zhang, Hongwei Li, Xinyue Hong, Yuan Jia

原文摘要

Long-term spacecraft autonomy requires embodied agents to use operational history under communication delays, time-critical operations, environmental uncertainty, complex physical interactions, power and computational constraints, and safety and reliability requirements. We introduce AstroMemBench and a Scenario-Challenge-Capability design method that maps these requirements to executable memory tests. The benchmark covers 16 memory subchallenges through 140 templates and 2240 instances across nine tasks in planetary surface operations, on-orbit servicing, and deep-space exploration. A deterministic environment scores actions for task completion, state changes, safety, and resource use. Ablations under nine conditions show that the benchmark is sensitive to how accurately agents recover and use historical information and to differences in memory-system design. Main experiments compare six configurations. Mem0, Chain-of-Memory, LightMem, and retrieval-augmented generation (RAG) improve same-session mean scores over native Pi Agent, with no clear advantage of the three memory systems over RAG. After a session restart, Mem0 outperforms RAG by 12.25 points. Benefits favor instruction retention and result reuse over ongoing state tracking and energy reservation. AstroMemBench supports the design, development, and selection of memory systems for embodied spacecraft agents. AstroMemBench 1.0 and its evaluation code are publicly available: https://github.com/flyingView/AstroMemBench-v1.

cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.04013

Pending Conflicts Make Progress Impossible

Petr Kuznetsov, Pierre Sutra, Guillermo Toyos-Marfurt

原文摘要

In this work, we study progress conditions for commutativity-aware, linearizable implementations of shared objects. Motivated by the observation that commuting operations can be executed in parallel, we introduce conflict-obstruction-freedom: a process is guaranteed to complete its operation if it runs for long enough without encountering step contention with conflicting (non-commuting) operations. This condition generalizes obstruction-freedom and wait-freedom by allowing progress as long as step contention is only induced by commuting operations. We prove that conflict-obstruction-free universal constructions are impossible to implement in the asynchronous read-write shared memory model. This result exposes a fundamental limitation of conflict-aware universal constructions: the mere invocation of conflicting operations imposes a synchronization cost. Progress requires eventual resolution of pending conflicts.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16589

STVLA: Spatial-aware and Temporal-memory forVision-Language-Action Models

Zhuo Han, Yeqiang Wang, Xinran Wang, Hongwei Han, Yi Xiong, Zunian Wan, Jianbo Mao, Huan Peng 等 11 位作者

原文摘要

Robotic manipulation often requires both spatial scene understanding and task history. However, many vision-language-action (VLA) policies rely primarily on current observations, without explicitly integrating spatial structure and task history. Appearance-based memories can be sensitive to occlusion and viewpoint changes, while spatial representations of the current scene alone do not preserve how the scene has changed during task execution. We introduce STVLA, a framework that incorporates features from a pretrained visual geometry foundation model into temporal memory alongside visual representations. This allows the policy to recall past scene structure as context for current action decisions. To select informative history, STVLA uses a mathematically grounded scoring rule that balances relevance to the current observation with complementary evidence from memory. Across evaluations on LIBERO, LIBERO-Mem, MIKASA-Robo, and SimplerEnv Bridge, STVLA achieves the highest average success rate among the compared methods on each benchmark. On real-world block stacking, object organization, and phone packaging, it improves success over $π_{0.5}$ by $58.33$, $30.00$, and $35.00$ percentage points, respectively. These results support integrating scene geometry into temporal memory to improve manipulation requiring both spatial precision and task history. Project website: \href{https://stvla.github.io/}{https://stvla.github.io/}

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16588

CrossPloit: A Benchmark for Cross-Version PoC Migration in Python Web Applications

Hung Nguyen-Huu, Nguyen Anh Tuan, Ting Zhang, Yue Liu, Chengran Yang, Lwin Khin Shar, Eng Lieh Ouh, David Lo

原文摘要

Affected-version ranges in vulnerability databases decide which deployments scanners, patching pipelines, and incident responders treat as vulnerable, yet these ranges are often incomplete, so vulnerable releases are silently treated as safe. Auditing them requires executable evidence: given a proof-of-concept (PoC) exploit for a listed version, show that the same vulnerability can be triggered on an earlier, unlisted version. This cross-version PoC migration combines source-code analysis, environment repair, and execution-based verification. We introduce CrossPloit, a benchmark of 127 migration instances across 45 CVEs in the Python web ecosystem. Each instance includes a version-pinned container, a reference PoC, and a vulnerability-specific oracle; two validators check that the observed effect arises from the disclosed vulnerability. Benchmark construction confirms 149 vulnerable prior versions absent from the affected-version lists of four databases, 127 of which require migration. Across 18 agent configurations (nine models and two harnesses), the best configuration resolves 58.3% of instances, compared with 8.7% for an adapted trace-guided baseline. Attack-vector shifts are particularly difficult: no configuration resolves more than 37.9% of these instances. Higher resolve rates are associated with a smaller share of missed-API failures and a larger share of incomplete executions among the remaining failures. In one configuration, withholding the reference PoC and its environment specification reduces resolve rate from 50.4% to 15.0%, highlighting the value of the supplied reference package. CrossPloit provides an execution-based measurement of under-reported exposure and of how far its enumeration can be automated.

cs.CR · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16586

Large Language Models as Digital Twins: a Distribution-Aware Questionnaire-Based Evaluation

Francesco Di Cursi, Chiara Boldrini, Andrea Passarella, Marco Conti

原文摘要

This paper investigates whether Large Language Models can answer a personality questionnaire as a specific individual would. Given a user's social-media posts, the model completes the BFI-2-60 Big Five questionnaire as that user. The predicted responses are converted into trait scores and compared with the user's actual scores. We evaluate GPT-4, Gemma, Llama 3.1, and Mistral on the MyPersonality dataset. To determine whether predictions reflect information from the user's writing, we compare them with answers produced without user text and with values derived from general population tendencies. We measure prediction error across all users and separately for individuals with low, intermediate, and high trait scores. We also test whether an LLM can express an explicit personality profile when given only the target Big Five scores, without user text, and examine how predictions change as more text is provided. The results show that user text does not consistently improve performance. Models perform better near the middle of the trait distribution, while at the lower and upper ends they often remain close to central values or perform well at one end but poorly at the other. When given an explicit profile, GPT-4 most clearly translates trait information into consistent questionnaire responses, particularly for Extraversion and Neuroticism. Increasing context does not systematically reduce error: open-source models fluctuate more, whereas GPT-4 remains comparatively stable. Overall, the evaluated models do not consistently reproduce the questionnaire behavior of specific individuals.

cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29922

Unintended Side Effects of Value Training in LLMs

Siddhesh Pawar, Chen Cecilia Liu, Anna Korhonen, Isabelle Augenstein

原文摘要

As LLMs are increasingly deployed in value-sensitive contexts, aligning them with human values has emerged as a post-training objective in NLP, aimed at instilling additional values and guiding model behaviour. However, current evaluations remain narrow. Because values are interconnected, it is unclear how fine-tuning for specific values alters unrelated capabilities and behaviours. In this work, we adapt four instruction-tuned LLMs across individual Hofstede's value dimensions and evaluate them on both cross-value transfer and broader behaviours. We find that value-specific training produces unintended side effects on untrained values, risk preferences, social cognition, and safety traits. Consequently, we suggest that value alignment after post-training should incorporate broader behavioral evaluations, and we highlight the need to investigate where additional value training might best fit into the pipeline. We release all 48 value-tuned model checkpoints at https://huggingface.co/sidicity/cultural-value-models

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10224

Jais 3: Introducing Arabic-centric Agentic AI

Team IFM, Haonan Li, Hadi Abdine, Nikhil Ranjan, Mohamed Anwar, Ahmad Chamma, Amr Mohamed, Ahmed Frikha 等 54 位作者

原文摘要

We present Jais 3, a fleet of six open, Arabic-centric agentic language models ranging from 0.9B to 375B parameters. Jais 3 is built to complete tasks for Arabic speakers, not only to answer questions in plain text: it is trained to plan multi-step tasks, call tools and Model Context Protocol (MCP) servers, operate a terminal, search the web, and read, edit, and produce PDF, spreadsheet, and presentation files, while communicating with the user in Arabic. Instead of pretraining from scratch, we adapt each model of the fully open K2 Horizon fleet to Arabic through continual pretraining, followed by supervised fine-tuning (SFT), final token preference optimization (FTPO), and reinforcement learning with verifiable rewards (RLVR). We combine natural Arabic text with translated, transliterated, and synthetic Arabic, and replay the base model's own training data to keep its reasoning, agentic, and English abilities. Jais 3 covers Modern Standard Arabic and the major regional dialects, follows instructions with Arabic-specific constraints, and inherits the 512K-token context window of K2 Horizon (all models except the 0.9B). To our knowledge, its 375B model is the largest open Arabic-centric language model to date. For evaluation, we revisit how Arabic models are assessed, moving to fully generative evaluation on audited Arabic benchmarks. Across model sizes, Jais 3 matches or outperforms open-weight models of comparable size on Arabic and agentic benchmarks, with particularly strong agentic capabilities and Arabic instruction following, and far fewer hallucinations. We release the weights and the checkpoints from every training stage of all six models, together with samples of the training data, the data recipes and training configurations, the training logs, the audited benchmarks, and the evaluation code.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.06900

ASAP: Assembly-Source Aligned Pseudocode Refinement For Binary Decompilation

Yujian Zhuang, Dehong Gao, Qichao Zhang, Qijing Lai, Jiaxin Wang, Libin Yang, Xiaoyan Cai

原文摘要

Large language models (LLMs) are increasingly used in binary decompilation to refine the C-like pseudocode produced by traditional rule-based decompilers. While this pseudocode is useful, it is a heuristic and lossy abstraction rather than a faithful copy of the source code. It often contains decompiler errors, especially for aggressively optimized binaries where critical low-level details are obscured. We present ASAP, an Assembly-Source Aligned Pseudocode refinement framework for binary decompilation. ASAP learns source-aligned assembly representations from paired source and binary functions using joint function-level and snippet-level contrastive alignment. A Q-Former then compresses chunk-level assembly features into a fixed number of assembly tokens. These tokens and the decompiler-produced pseudocode jointly condition the decompilation LLM. During refinement, we use stochastic pseudocode masking and a relative assembly-advantage loss to discourage the model from relying solely on pseudocode. On two decompilation benchmarks across multiple compiler optimization levels, ASAP improves the average re-execution rate from 64.7% to 71.9% and the average recompilation rate from 91.6% to 96.6% compared with the strongest baseline. Our approach offers both a new perspective and a practical solution to binary decompilation.

cs.PL · cs.CR · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16583

ReFineR: Single image to dense point cloud registration at pixel level with point splatting

Ugo Insalaco, Jean-Emmanuel Deschaud, Pierre Dellenbach

原文摘要

Cameras and LiDARs (Light Detection and Ranging) provide complementary observations of the same scene: cameras capture rich appearance cues while LiDAR sensors provide accurate geometric measurements. In this work, we address the problem of fine image-to-point cloud registration from a single image and a roughly estimated camera pose. On robotic platforms, residual reprojection errors of several pixels are commonly observed even after calibration due to temporal desynchronization, mechanical vibrations, or inaccuracies in point cloud reconstruction. Such errors can lead to decimeter-level geometric discrepancies at moderate distances and significantly affect multiple downstream tasks, such as dense cross-modal supervision, texture mapping, and semantic label transfer. To address this challenge, we introduce ReFineR: an offline framework to finely register individual images to pre-reconstructed point clouds. ReFineR builds upon differentiable point splatting rasterization and a smooth Gradient Orientation Measure (GOM) to iteratively refine the camera pose through analysis-by-synthesis optimization. We evaluated the method on a curated dataset of manually calibrated images collected from heterogeneous data sources. Furthermore, we show that an automatic point cloud rescale factor can be estimated prior to optimization, enabling robust refinement across a wide range of scenarios using a single scale-invariant formulation. The project page can be found at https://refiner-project.github.io/

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16581

Do the Details Help the Whole? One-Way Transfer from Person Actions to Group Intent in VLMs on JRDB-Social

Chengguang Gan, Zhixi Cai, Yunhao Liang, Hanjun Wei, QingHao Zhang, Shiwen Ni

原文摘要

Social scene datasets label people at two levels: what each person is doing and what the group is there for. Training on both levels at once is often assumed to help each of them. We test this assumption for vision-language models (VLMs) on JRDB-Social, treating person actions as the details and group intent as the whole. We fine-tune Qwen3.5 4B under eight training arms with three seeds, repeat the core arms on Qwen3.5 9B and Qwen3-VL 4B, and judge every arm with a verdict rule fixed before testing. Writing each member's actions before the intent raises intent macro-F1 from 30.1 to 31.0, and writing the intent first reaches 31.2, while action F1 stays within 0.2 points of an action-only model. Under the selected recipe, no arm trained with intent improves actions. The gain requires correct, image-grounded actions: empty action lists or actions borrowed from another group with the same intent lower intent F1. Forced-prefix tests, occlusion, member masking, linear probes and component grafting show that the intent follows the written actions, relies more on people and partly resides in the vision tower. Supplying actions as input instead of as targets hurts both levels.

cs.CV · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.14198

Multimodal Zero-Shot Prediction for Transaction Data at Scale

Parameswaran Kamalaruban, Viktor Drobnyi, Maeve Madigan, Julia Rozanova, David Sutton, Stuart Burrell

原文摘要

Sequential financial transaction data are increasingly being used by financial institutions to train payment foundation models at scale, producing rich contextual embeddings that support applications such as fraud prevention, credit risk assessment, and offer personalization. However, such models are not designed for flexible instruction-following reasoning and require bespoke downstream models for each application. In contrast, large language models (LLMs) provide a natural-language interface for flexible transaction reasoning, yet naively serializing transaction histories into text representations is inefficient and costly. Multimodal fusion architectures bridge these paradigms, injecting transaction embeddings directly into flexible language reasoning pipelines. In this work, we present a systematic study of how transaction embeddings, pretrained at the scale of a global payments network, interact with LLM-based reasoning through embedding injection, transaction-language alignment, and instruction tuning. To do so, we train the Multimodal Instruction Network for Transactions (MINT), which combines a transaction encoder pretrained on billions of real-world payments with multimodal instruction tuning on millions of transaction question-answering examples. We evaluate predictive and extractive question answering, zero-shot generalization to unseen question types, and inference efficiency. Our results reveal a consistent trade-off: compact transaction embeddings improve predictive QA accuracy while substantially reducing input tokens, latency, and memory relative to a text-serialized baseline, whereas serialized histories remain highly competitive for extractive QA. Overall, our empirical findings provide a set of actionable insights and practical guidance for building multimodal transaction-aware language systems at network scale.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16579

ActWorld-JEPA: Action-Conditioned JEPA World Models Meet Latent Planning for End-to-End Autonomous Driving

Ziying Song, Shengkai Zhang, Caiyan Jia, Lei Yang, Lin Liu, Yuchen Liu, Ziyang Liu, Zirui Li 等 13 位作者

原文摘要

Joint-Embedding Predictive Architectures (JEPAs) enable future prediction in latent space without pixel reconstruction. However, latent predictive consistency alone does not establish the relative driving quality of candidate plans. We present ActWorld-JEPA, a joint-embedding formulation for candidate-based driving. Where Plan-Conditioned JEPA (PC-JEPA) uses global and step-wise trajectory intentions to guide recurrent latent prediction, then combines the predicted future with the current scene through gated cross-attention; Relational Residual Reranking ($R^3$) models agreement and discrepancy between plan and future representations under planning supervision, yielding bounded, gated residual corrections to the base planning scores. This formulation connects latent prediction to candidate selection using only observed context and proposed trajectories at inference. Benchmark-specific implementations report 94.1 PDMS on NAVSIM v1 navtest, with the final selector calibrated using aggregate NAVTEST feedback, and 91.6 and 42.7 EPDMS on NAVSIM v2 navtest and navhard, respectively. The nuScenes evaluation reports a mean $L_2$ error of 1.20 m and a predicted collision rate of 0.76% over 1--6 s. This work provides an effective path from JEPA's predictive representations to driving decisions informed by anticipated consequences.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24122

HAVANA: A Hierarchical Video-Action Model for Vision-Language Navigation

Dongzhihan Wang, Yi Du, Chen Wang, Xiuxian Li, Liang Xu

原文摘要

Vision-Language Navigation (VLN) requires long-horizon instruction-consistent planning and short-horizon path planning grounded in the currently observed geometry. Anticipating how future observations evolve along the instructed route provides predictive guidance beyond the current field of view, motivating video-action models (VAMs) that learn representations of future visual states. However, many existing VAMs couple future visual prediction with action generation during inference, despite their distinct temporal requirements: instruction-conditioned route guidance can be reused across multiple control steps, whereas local trajectories must continuously adapt to fresh observations. To bridge this gap, we introduce HAVANA, a dual-system VAM that decouples predictive reasoning from reactive local planning. A slow system produces a compact language-conditioned latent plan from observation history, while a fast system combines the latest plan with the current observation to generate local trajectories. Their asynchronous execution enables rapid adaptation without repeated future prediction, and no future video is generated during inference. Experiments on OmniNavBench demonstrate state-of-the-art navigation performance over prior baselines, while zero-shot real-world deployments further show robust generalization to unseen environments.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.05615

Classes Testable with $O(1/ε)$ Queries for $ε$ Below an $n$-Independent Threshold

Nader H. Bshouty, George Haddad

原文摘要

In this paper, we study classes of Boolean functions that are testable with $O(ψ+1/ε)$ queries, where $ψ$ depends on the parameters of the class (e.g., the number of terms, the number of relevant variables, etc.), is independent of (or sublinear in) $1/ε$, and is independent of the total number of variables $n$. In particular, when $ε$ is sufficiently small so that $ε=O(1/ψ)$, the query complexity is $O(1/ε)$, matching the known tight lower bound $Ω(1/ε)$. In particular, if the structural parameters of the class are fixed and $ε=1/ω_n(1)$ (e.g., $1/\log n$), then the asymptotic query complexity is $O(1/ε)$. This result was previously known for classes of terms of size at most $k$ and exclusive OR functions of at most $k$ variables. In this paper, we extend this list to include the classes: $k$-junta, functions with Fourier degree at most $d$, $s$-sparse polynomials of degree at most $d$, and $s$-sparse polynomials. Additionally, we show that for any class $C$ of Boolean functions that depend on at most $k$ variables, if $C$ is properly exactly learnable, then it is testable with $O(1/ε)$ queries for $ε<1/ψ$, where $ψ$ depends on $k$ and independent of the total number of variables $n$.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33457

A Free Knob: A Sensitivity Audit for Calibration in Threshold-based Evaluation

Md Tanveer Hossain Munim, Bijoy Ahmed Saiem, Al-Amin Sany, Tanzima Hashem

原文摘要

Many benchmarks for rare events score a prediction in three steps: pool the prediction and the target over spatial blocks, threshold each pooled field, and score the resulting contingency table. We show that at a fixed rare threshold the max-pooled Critical Success Index (CSI) cannot separate two different things: how well a system places events, and how well its amplitudes are calibrated. Sharp observations push many blocks above the threshold, while the attenuated predictions of squared-error regression leave those same blocks below it. A monotone calibration map is fitted on held-out data and applied separately to each system. The map needs no retraining and cannot change the order of any two pixels, so whatever part of a difference it reproduces cannot be evidence of better spatial ranking. We report how much of a reported difference the map removes. On SEVIR, two released checkpoints of one architecture differ by -29.5% in extreme-threshold CSI before the control and by +5.3% after it. Across 450 pairwise contrasts among 6 systems, how far apart two systems sit in pooled frequency bias predicts how far their CSI difference moves under the control (r = 0.896 when the map is fitted after pooling), and 64 contrasts reverse sign. At CasCast's published extreme-event operating point, calibration alone removes 88%-106% of the cascade's gain over its backbone across three controls. Calibration also reveals advantages hidden by a better-calibrated baseline. On geostationary infrared imagery, a larger share of the score can be explained by calibration as events get rarer. The problem therefore needs two things at the same time: a threshold that is fixed in advance, and a model whose output is poorly calibrated at that threshold. We recommend reporting pooled frequency bias and a symmetric held-out FreeKnob Audit alongside rare-event pool-and-threshold scores.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09195

InstantHMR : Real-Time Human Mesh Recovery on Your Phone

Mohamed Adjel, Kahina Chalabi, Vincent Bonnet

原文摘要

Real-time 3D human mesh recovery from a single image on mobile devices is a challenging problem with many applications, including robotics, biomechanics, AR/VR, and exergames. Current state-of-the-art models are either too slow to run efficiently on mobile devices or sacrifice accuracy for speed. We present InstantHMR, a family of open-source single-image regressors suited for mobile inference, based on the Momentum Human Rig (MHR) parametric model and trained exclusively on publicly available annotations from the SAM 3D Body dataset. Our approach achieves remarkable performance on a Samsung Galaxy S23 NPU, recovering a 6,890-vertex body mesh in 5.5 ms per person, and 0.98 ms on an NVIDIA RTX 4070 GPU. This speed surpasses all existing methods we evaluated. On mobile, it is 12x faster than the single mobile-compatible method that slightly outperforms ours in accuracy, while achieving competitive accuracy with 40.0 mm PA-MPJPE on 3DPW, 48.8 mm on EMDB and 41.7 mm on RICH. To ensure fair comparisons, we re-evaluate 11 state-of-the-art methods under a unified protocol aligned with the closest MHR-based competitors, including Meta's official MHR-to-SMPL conversion, and report their reproduction results. Additionally, we measure latency across eight different targets, ranging from desktop TensorRT to two phone NPUs. Code and models are available at https://github.com/Naturalpad-studio/InstantHMR.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16574

HALight: Heterogeneous Multi-Agent Reinforcement Learning for Traffic Signal Control under Lane-Level Missing Observations

Chenkai Hu, Yingqian Zhang, Ming Xie, Yi Fang, Chengxiang Jin, Chao Li

原文摘要

Multi-agent reinforcement learning has shown great promise for traffic signal control. However, most existing methods rely on two key assumptions: (i) agents are assumed to be homogeneous with unified observation/action spaces; (ii) full and reliable lane-level traffic observations are available to all agents. In practice, urban road networks exhibit substantial structural heterogeneity (e.g., lane configurations and phase designs), while sensor failures and incomplete detector coverage often result in missing incoming lane measurements. Such issues can severely impair coordination and destabilize learning. To address these challenges, we propose HALight, a communication-enhanced heterogeneous multi-agent reinforcement learning framework for traffic signal control under lane-level missing observations. HALight supports intersection-specific observation and action modeling to accommodate heterogeneous intersections, and incorporates a lightweight one-hop neighborhood communication module to enrich spatial context when lane-level observations are partially unavailable. Moreover, HALight adopts a sequential policy update scheme to improve coordination among heterogeneous agents. Experimental results on two real-world road networks demonstrate that HALight outperforms representative homogeneous and heterogeneous baselines in terms of traffic efficiency and convergence behavior. The code and datasets used in this work are available at https://github.com/Huchenkaii/HALight.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.24195

ZFLean: a framework for set-level mathematics in Lean

Vincent Trélat, Matteo Pouillat

原文摘要

We present ZFLean, a Lean 4 library for doing core mathematics inside a model of ZFC with the ergonomics expected of typed Mathlib developments. Building on Mathlib's ZFC model, we contribute a relational calculus for sets with rewriting hints and small predictable tactics, canonical set-theoretic constructions -- Booleans, naturals, integers, sums/option -- and bridges between ZFC objects and Lean's native types enabling mixed set-level/typed proofs. The layer reduces boilerplate for extensional reasoning while remaining compatible with vanilla Mathlib. We discuss library organization and usage patterns that lower the friction of set-theoretic formalization in a dependently typed assistant. We demonstrate typical use of the framework with a case study exercising our constructions and relational calculus through a proof of an isomorphism theorem on curried functions.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16573

Tight Cycle-Cover Inequalities and Improved Approximation for Shortest Common Superstring with Reverse Complements

Ryosuke Yamano, Tetsuo Shibuya

原文摘要

The Shortest Common Superstring (SCS) problem is a classical idealized model of genome assembly. However, SCS does not account for the double-stranded nature of DNA: sequencing reads may originate from either strand without a known orientation. This motivates a natural variant, Shortest Common Superstring with Reverse Complements (SCS-RC), which asks for a shortest string containing each input string or its reverse complement as a substring. For SCS, the MGREEDY algorithm constructs an optimal cycle cover of the overlap graph and concatenates the strings obtained by breaking each cycle at its minimum-overlap edge. This algorithm was recently shown to achieve a tight $3$-approximation. Its reverse-complement extension, MGREEDY-RC, also constructs an optimal cycle cover of the overlap graph augmented with reverse complements, but its best known approximation guarantee was $3.75$ (CPM 2026). We extend this cycle-cover inequality to SCS-RC and show that MGREEDY-RC achieves a $3$-approximation, which is tight even over the DNA alphabet. The $3$-approximation guarantee for MGREEDY-RC yields a $2.5$-approximation for TGREEDY-RC, which greedily merges the strings derived from the cycle cover. By replacing this greedy merging step with an algorithm based on a reduction to a maximum-weight Hamiltonian path problem constrained to contain a given perfect matching, we obtain a $(22/9+\varepsilon)$-approximation for every fixed $\varepsilon>0$. This improves upon the previous best ratio of $8/3$ (WABI 2026), establishing the best known polynomial-time approximation guarantee for SCS-RC.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.20668

BELLS-O: Evaluating the Operational Trade-offs of LLM Supervision Systems

Leonhard Waibl, Felix Michalak, Hadrien Mariaccia

原文摘要

LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI applications, yet existing benchmarks are often vendor-biased, omit cost and latency, and rarely compare specialized guardrails against repurposed generalist LLMs. We present BELLS-O (Benchmark for the Evaluation of LLM Supervision Systems, Operational), an independent operational benchmark that evaluates 33 systems from 17 developers: specialized guardrails (e.g., Llama Guard 4, ShieldGemma-2B, Lakera Guard) and frontier generalists repurposed as supervisors (e.g., GPT-5.4, Claude Sonnet 4.6, Grok 4.1 Fast), jointly on detection rate, false-positive rate, latency, and cost. We cover input/output moderation across 11 harm categories, jailbreak detection across 13 attack techniques, and prompt-injection detection, using in-house datasets built from handcrafted prompts, curated external samples, and quality-controlled synthetic generation, with synthetic moderation samples paraphrased to suppress generator fingerprints. Content-moderation runs are repeated five times and all rates reported with 95% intervals. The Pareto frontier reveals use-case-dependent tradeoffs. On content moderation, specialized supervisors are operationally ahead: top systems match the best generalist LLMs on detection (93-96%, no significant difference) at low false-positive rates while running ~5x faster; cost depends more on serving setup than on class. On jailbreak detection, generalist LLMs achieve higher median detection on three of four datasets and lower false-positive rates, but at higher latency and, for closed frontier models, 10-100x higher cost. On prompt injection, the two best dedicated detectors lead. We release the framework, an interactive leaderboard, and four datasets as a vendor-neutral basis for selecting safeguards under deployment constraints.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.22791

R Package iglm: Regression under Interference in Connected Populations

Cornelius Fritz, Michael Schweinberger

原文摘要

We introduce R package iglm, which implements a unifying regression framework for studying relationships among binary, count-, and real-valued predictors and outcomes in connected populations with interference due to spillover, contagion, and other complex phenomena. On the computational side, the regression framework provides scalable methods that can be applied to small and large data sets, by solving a convex optimization program based on pseudo-likelihoods via accelerated Minorization-Maximization algorithms. On the statistical side, the regression framework comes with provable theoretical guarantees for statistical estimators, including consistency results and rates of convergence for maximum pseudo-likelihood estimators. To increase the versatility of iglm, users can add custom-built model terms. We showcase iglm using data on hate speech on the social media platform X and communications among students.

stat.CO · cs.SI · stat.OT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.04978

A Tactile-Informed Bi-Level Framework with Complementary Motion-Contact Planning and Tracking for Robust Model-Based In-Hand Manipulation

Yongpeng Jiang, Mingrui Yu, Yi Ren, Xiang Li

原文摘要

In dexterous in-hand manipulation, learning-based methods achieve strong task performance with increasing generalization to unseen objects. A complementary advantage of model-based methods, central to this work, is online adaptation to new task specifications without policy retraining, given suitable system models. However, due to the complexity of physical contacts and limited sensing modalities, existing model-based methods still struggle with modeling errors, even when using advanced contact models or optimization schemes, which limit their real-world applications. To advance the effectiveness and robustness of model-based contact-rich in-hand manipulation, this paper proposes a novel bi-level framework with tactile incorporation to mitigate these limitations. Specifically, at the high level, reference finger motions and contacts are jointly generated via contact-implicit model predictive control. The high-level module enables online reactive planning and disturbance recovery. At the low level, these references are concurrently tracked through a hand force-motion model and tactile feedback. The low-level module compensates for modeling errors and enhances manipulation robustness. The complementarity between the two levels is manifested in two dimensions: 1) subsystem decomposition, with a slow optimization-based planning layer and a fast tactile-reactive control layer; and 2) functional decomposition, combining task-level optimality with contact-level stability. Extensive experiments demonstrate that our approach outperforms existing model-based methods in accuracy and robustness, while supporting online replanning during execution. Our method successfully completes a diverse set of challenging tasks in real-world environments, even under appreciable external disturbances and modeling uncertainties.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.21426

Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking

Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy

原文摘要

Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.

cs.LG · cs.IT · eess.SP · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02282

POIROT: Agents as their own auditors for failure attribution in large language model multi-agent systems

Iñaki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal, J. M. Valverde-García, Annemarie F. Laudanski, Álvaro Gutiérrez, Eduardo Rocon, Manuel Cebrian

原文摘要

Large language model (LLM)-based multi-agent systems (LLM-MAS) are kept out of safety-critical domains by emergent failures that are hard to detect and to attribute. Current evaluation relies on a centralised evaluator, human or LLM judge, that must master the domain and hold the whole interaction history in one context. We propose that the agents executing a task can themselves attribute its failures. We introduce POIROT (Phased Oversight via Introspection, Review and Opinion Tallying), a modular protocol in which each agent casts a private vote within a hazard space derived from the system architecture, and the votes are aggregated into a single attribution. Before the vote, phases are added that progressively increase the interaction between agents, from independent self-assessment to sharing the self-assessments and peer interrogation. We build the protocol phase by phase, measure the contribution of each phase through an ablation, and compare every configuration with a single-LLM evaluator. We evaluate one proprietary and two open-weight LLMs on the Who&When benchmark and on BLAME (Benchmark for Localising Agent Malfunctions Effectively), a new benchmark for fault attribution in clinical rehabilitation and algorithmic trading systems. Agent-based attribution outperforms the single-LLM evaluator, but the benefit comes from independent self-assessment, not from interaction: sharing the assessments makes agents converge on the same, often wrong, culprit, and interrogation does not recover the loss. Without sharing and interrogation, the protocol outperforms the evaluator in all nine model-benchmark configurations (up to +50 percentage points). POIROT and BLAME are released as open source.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16566

Towards Automated Recommendation of Steady-State Detection Algorithms for Software Performance Benchmarks

Martin Beseda, Vittorio Cortellessa, Federico Di Menna, Daniele Di Pompeo, Luca Traini

原文摘要

Steady-state detection is a central step in software performance benchmarking, yet different detection methods rely on different assumptions and may perform best on different execution profiles. This paper formulates steady-state detection as an algorithm-recommendation problem: given a benchmark time series, predict which of two complementary detectors is more appropriate. The first, CP-SSD, identifies steady state through changepoint-based segmentation of the execution trace, whereas the second, KB-KSSD, uses a kernel-based approach to identify stable behaviour. We propose a data-driven recommender that characterises benchmark time series using automatically extracted features and combines supervised prediction with information derived from groups of time series exhibiting similar behaviour. The approach is evaluated on 407 labelled benchmark time series for which one of the two detectors can be identified as preferable with respect to a reference steady-state index. The proposed recommender selects the better detector correctly for 72.97% of the series, compared with 62.65% when always selecting the detector that performs best most often, corresponding to an absolute improvement of 10.32 percentage points. Paired statistical testing and bootstrap confidence intervals support this improvement. A comparison with a recommender using time-series features alone shows that the cluster-derived information mainly improves interpretability rather than predictive accuracy. Recommendations also remain highly stable when the unsupervised representation is learned from either the labelled subset or the full collection of 5,860 time series. These results demonstrate the feasibility of per-series steady-state detector recommendation while highlighting the need for broader labelled data, particularly for execution profiles favouring the changepoint-based detector.

cs.PF · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16565

Look Here, Then Decide: Small Decision Models in a Harness Find Memory-Safety Vulnerabilities at Scale on Commodity GPUs

Tim De Wachter, Kyryl Volkov, Myrto Eleftheria Gkogkou, Mathieu Huysman

原文摘要

We ask whether small, open-weight decision models can support vulnerability discovery in open-source software at scale and what the surrounding system must provide. These models answer a typed question in one forward pass, returning scores for the permitted answers rather than text. Our harness selects widely packaged C and C++ projects and uses deterministic incomplete-fix miners and shape localizers to decide where to look. Intern-Decision-4B, a 4B-parameter open-weight decision model, then scores every candidate function. The scores rank the queues rather than accept or reject candidates, apart from a low reach-score floor that some queues kept. The search uses no frontier model. Alone, the model is weak: it separates vulnerable from benign functions with AUC 0.73, but a vulnerable function from its own patch with only 0.56. It is more useful for ordering candidates. On 75 public CVEs from projects without any of our findings, the fixed function reached its repository's top 25 in 21% of cases, 1.4 times as often as under random order of the same queues. Recall is bounded mainly by where the localizers look. On the campaign's own queues the model's order placed 7.5 times more confirmed findings in the top 10 than random order, an observed, selection-biased upper bound. On three GB10 systems, the harness searched more than 4,000 projects at 900-1,100 repositories per hour. The campaign confirmed 187 memory-safety findings; by worst-case CVSS v3.1, 12 are critical, 148 high, 23 medium and 4 low. The decision step runs at 4.4 decisions per second on one GB10 and 9.8 on a consumer RTX 4090 gaming PC, with matching scores. A small decision model supports vulnerability discovery through prioritization, while deterministic analysis and verification remain essential. All confirmed findings are being disclosed to maintainers under coordinated disclosure before publication.

cs.CR · cs.LG · cs.SE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.12586

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng, Ze-wen Li, Yan-zhe Hou

原文摘要

Pixel-level supervision in medical image segmentation ignores the geometric priors of the target region. Chan-Vese losses restore region-level priors and boundary regularity but do not characterise local boundary geometry. We propose the Deep Active Contour and Mean Curvature (DACMC) loss, which embeds the mean curvature of the predicted boundary in the Chan-Vese region functional; a fixed fourth-order-accurate convolution kernel evaluates curvature without explicit differentiation. The loss adds no trainable parameters and no inference cost, increases training time by 4.8%, and uses one hyper-parameter lambda = 10^-3 shared by all settings. We evaluate DACMC on liver CT, spleen MRI and ISIC dermoscopy images with U-Net and CE-Net backbones, four metrics (DSC, HD95, JS, ASD) and four competing losses under an identical protocol, together with a five-point lambda sweep, a wall-clock cost measurement and a five-seed replication with paired Wilcoxon signed-rank tests. DACMC ranks first or second in Dice in five of the six dataset-backbone settings, most consistently in distance metrics. On spleen MRI it reduces HD95 and ASD to 16.28 and 1.90 mm, and on ISIC dermoscopy it reduces HD95 to 7.08 mm. Dice varies by 0.0023 across five lambda values spanning three orders of magnitude; degradation appears only when curvature dominates. Five-seed replication shows that curvature stabilises training and removes occasional severe failures, although mean differences from the closest competing loss do not reach statistical significance at five seeds. DACMC imposes second-order geometric regularity on segmentation boundaries at low overhead, with the clearest benefit for small or scale-varying targets. Limitations include dataset size and partitioning, classical backbones and losses, and restriction to 2-D single-organ images.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16562

The Regular Cover Problem is NP-complete

Stefan Hetzl, Parham Shadman

原文摘要

The regular cover problem is the following question: given a finite language $L$ and a $k\geq 0$, is there an acyclic regular grammar $G$ with at most $k$ productions that covers $L$, i.e., that satisfies $L(G) \supseteq L$. The study of this problem is motivated by applications in proof theory and automated deduction. The regular cover problem is known to be in NP. In this paper we show that it is NP-complete by reducing the vertex cover problem for graphs with maximum degree three to it.

cs.FL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16561

SPARC: Accurate Photonic Matrix Multiplication via Representative Vectors

Yasuhiro Fujiwara, Tatsuhiro Nakamori, Hideyuki Kawashima

原文摘要

Photonic neural network accelerators have emerged as a promising solution for reducing the power consumption of deep neural network (DNN) inference by executing matrix multiplication on analog photonic hardware. However, their inference accuracy is limited by low-bit quantization required for photonic processing and by analog photonic noise. This paper proposes an approach for improving inference accuracy by exploiting the high similarity among activation vectors in DNNs. Specifically, our approach constructs a representative vector for each group of similar activation vectors, computes its inner product digitally with high precision, and offloads the residual inner products to the photonic accelerator. Since the activation vectors within each group are highly similar, the residual vectors have much smaller norms, thereby reducing the effects of quantization errors and analog photonic noise. Furthermore, our approach preserves the photonic inner-product computation pipeline, allowing seamless integration into existing and future photonic accelerators without hardware modification. Experimental results on six representative DNN models demonstrate that the proposed approach can improve inference accuracy across diverse network architectures and application domains.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16560

When and How SignSGD Outperforms SGD: A Functional Scaling Law Perspective

Zilin Wang, Binghui Li, Jia-Nan Wang, Lean Wang, Jinbo Wang, Lei Wu

原文摘要

We study how the sign operator interacts with gradient noise to shape the learning dynamics and scaling behavior of sign-based optimizers. In power-law kernel regression with diagonal features, we establish a functional scaling law (FSL) for SignSGD under general learning rate schedules. The FSL describes the learning dynamics through two processes: signal learning and noise forgetting. The key mechanism is noise-induced preconditioning: gradient noise smooths the sign operator, inducing an effective preconditioned SGD. Noise-curvature alignment enables this preconditioning to transform the effective Hessian from $\mathbf{H}$ to $\mathbf{H}^{1/2}$. The resulting dynamics exhibit faster signal learning but slower noise forgetting than SGD. This tradeoff yields a regime-dependent comparison with SGD: (i) SignSGD achieves improved data efficiency in the hard-task regime; (ii) both optimizers achieve the same data efficiency in the easy-task regime, but SignSGD maintains this efficiency at larger batch sizes, requiring fewer optimization steps for a fixed data budget. Power-law kernel regression experiments support the proposed mechanism and predicted scaling behavior. LLM pre-training experiments further suggest that the SignSGD FSL better captures the batch-size dependence of loss dynamics for sign-based optimizers than the original SGD FSL.

stat.ML · cs.LG

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arXiv ↗PDF ↗