Artificial Intelligence · Cross-listed from cs.RO
Achieve What You Imagined: Learning to Align Actions with Visual Plans
Yuheng Qiao, Ziran Wei, Xiaohan Wang et al. (+5 more)
World-action models can jointly predict future visual observations and
robot actions. However, discrepancies may exist between their visual predictions and the consequences implied by generated actions. We observe that WAMs can often generate visually plausible task-completion outcomes before producing action sequences that reliably achieve them. Consequently, we treat the WAM-generated visual prediction as a goal-conditioned visual proposal rather than a directly executable plan. We use a frozen action-conditioned world model to predict action-conditioned consequences and construct feedback based on consistency between the two future predictions and
alignment with the terminal goal. Leveraging this feedback, we employ Flow Policy Optimization (FPO) to optimize the action head of the WAM. This framework avoids online
robot interaction and additional training of task-specific reward models. Across four real-world UR5 manipulation tasks, our method increases the mean success rate from 43.4% to 75.1%, compared with 61.4% for $\pi_{0.5}$. These results show that cross-model prediction discrepancy can provide useful feedback for improving
robot policies under the evaluated manipulation tasks. Website: https://imagine-to-achieve.github.io/
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All authors Yuheng Qiao, Ziran Wei, Xiaohan Wang, Daqiang Guo, Yichen Luo, Zhibo Pang, Peng Zhou, Sichao Liu
Artificial Intelligence
Vestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory
Jayant Parashar, Eugene F. Douglass, William C. Bastian et al. (+1 more)
An
agent harness controls how a
language model accesses information, uses tools, preserves
memory, and checks its work. Improving this software is costly when each evaluation requires a long interaction with an environment. We introduce Vestrum, a framework that turns failures in execution traces into scoped harness changes without training the task model. Its organizing overhypothesis is that tasks of a shared kind may exhibit recurring failures whose remedies transfer within that kind. Vestrum expresses failures as recognizable classes, proposes changes across verification,
retrieval, decomposition, and knowledge synthesis, and screens their scope before evaluating them as a bundle. A persistent lessons file informs subsequent proposals. Across five settings and two baseline harnesses, the frozen harnesses improve held-out performance: UltraHorizon rises from 47.6 to 59.8 over GAM, Terminal-Bench 4 Hard from 63.7% to 70.3% of checks passed over Claude Code on eight held-out tasks at 1.03x test cost, and
cell-type annotation agreement from 67.5% to 77.8% on held-out sections of one slide, alongside gains on LoCoMo and AMA-Bench. Across our searches, verification grounded in evidence helped both intermediate steps and final answers, at lower cost at intermediate steps, while critics asked to rebuild finished answers broke more than they repaired. On the three
memory benchmarks, Vestrum also scores above the evaluated GEPA configurations in every paired evaluation.
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All authors Jayant Parashar, Eugene F. Douglass, William C. Bastian, Suchendra M. Bhandarkar
Artificial Intelligence · Cross-listed from cs.CV
Augmenting Visual Anomaly Detection with Automated Interpretability
Antonio De Santis, Arsenio Leo and Marco Brambilla
Visual anomaly detectors identify deviations from known-normal data, but their anomaly signals may mix evidence of actual anomalies with benign visual variation. We investigate whether automated interpretability can augment visual anomaly detectors by identifying and intervening on different components of this signal. We decompose PatchCore nearest-normal residuals into sparse features using Sparse Autoencoders (SAEs), and provide high-activation and contrastive non-active examples to a
Multimodal LLM, which describes each feature and labels it as anomaly, distractor, or uncertain. These labels guide interventions in the SAE hidden representation, where distractor features are suppressed and anomaly features amplified. The edited representation is then used to reconstruct patch
embeddings, which are rescored with PatchCore. Across 40 categories from four
benchmarks, applying both interventions jointly improves macro-average image-level AUROC from 0.8724 to 0.8857 on source data and from 0.8066 to 0.8210 under synthetic corruptions. On three additional RobustAD categories with real acquisition shifts, the same interventions improve AUROC from 0.8745 to 0.9056 on source data and from 0.6069 to 0.6599 under real acquisition shifts. Finally, individual feature interventions across all 43 categories show that the MLLM labels are aligned in aggregate with how features differently affect normal and anomalous images.
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Artificial Intelligence
Dual-Vocabulary Language Model for Cross-Tokenizer Distillation
Kedi Chen, Chen Lin, Yutao Sun et al. (+1 more)
On-policy
distillation (OPD) bridges teacher supervision and student behavior, but different teacher-student tokenizers introduce misalignment in both input tokenization (#1) and output logits (#2). Existing approaches address the former by matching same-text spans or converting
tokens to bytes, often losing fine-grained
token information or disrupting the native-
token paradigm, while for the latter, strategies such as ranking, padding, or key-
token selection retain only shared logit dimensions, resulting in much distribution loss. In this paper, we propose Dual-Vocabulary
Language Model (DVLM), which replaces the teacher's LM head with a new student-vocabulary projection head and obtains full-dimensional student logits (for #2). To support student
tokens (for #1), it takes a Parallel-Tokenized Sequence (PTS) as input, which concatenates the original teacher-tokenized sequence and a re-tokenized sequence formed by independently converting each student
token into a teacher-
token group. To avoid inference inconsistency with the original teacher
tokens, the Hybrid-Prefix
Attention (HPA) further restricts re-tokenized groups to their corresponding teacher prefix and uses its last state as the aggregation of the original student-
token representation for projection into the student vocabulary space. Similarly, via the combined use of PTS and HPA, the DVLM teacher can provide distribution-aligned supervision with the student's input-tokenization and output-logit during OPD. Experimental results demonstrate that our DVLM teacher has a similar converged loss as the original teacher model and enables student models to improve performance across six
reasoning tasks.
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All authors Kedi Chen, Chen Lin, Yutao Sun, Wei Zhang
Artificial Intelligence · Cross-listed from cs.RO
CodeActionBench: Evaluating Agentic Code-as-Policy for Embodied Manipulation
Yiheng Lyu, Xueying Jiang, Wenhao Li et al. (+2 more)
How well can general-purpose
multimodal models turn visual understanding and
reasoning into
embodied manipulation via executable code? We introduce CodeActionBench, a
benchmark of 25 manipulation tasks that evaluates this capability through agentic Code-as-Policy. Without task-specific fine-tuning, demonstrations, external specialist perception or grasp modules, privileged scene state, or predefined task policies,
agents should select visual evidence, form task-relevant 3D estimates, construct manipulation targets, and iteratively execute and revise their policies. A shared
robot API provides RGB observations, calibrated
geometric operations,
robot feedback, and bounded motion, leaving task-dependent decisions to the evaluated
agent. Fixed task instances, resource budgets, and a hidden physical-outcome verifier support controlled comparisons across models and harness configurations. Extensive evaluations across nine configurations and 675 attempts achieve success rates ranging from 2.7% to 73.3%. The strongest configuration, GPT-6 Astra with Codex CLI, solves 22 of 25 tasks at least once in three attempts, demonstrating the best performance while still leaving substantial room for improvement. Trajectory analyses reveal difficulties in spatial
alignment, object retention, and completion judgment, including task failures despite successfully completed motions. CodeActionBench provides a controlled testbed for measuring how general-purpose models translate their capabilities into manipulation behavior and for examining typical failure scenarios in that process.
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All authors Yiheng Lyu, Xueying Jiang, Wenhao Li, Shijian Lu, Gongjie Zhang
Artificial Intelligence · Cross-listed from cs.LG
MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception
Yuhao Li, Louie Hong Yao, Tianyi Shi et al. (+4 more)
Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structure broadly through physics-motivated dynamical formulations or learning-motivated architectures. The former provide stronger priors but may constrain flexibility, whereas the latter are more flexible but leave the spatiotemporal coupling largely implicit. We therefore seek an approach that combines flexible learning with an explicit
geometric bias for jointly modeling time and space. To this end, we propose Minkowski Positional Encoding (MinkowskiPE), which uses joint temporal and spatial coordinates to parameterize Lorentz transformations applied to query and key features. With MinkowskiPE, the query-key
attention score depends on position only through the relative spacetime displacement between the two
tokens and is therefore invariant to global translation of the coordinates. This paradigm retains the standard dot-product
attention interface and remains compatible with efficient
attention implementations. We evaluate MinkowskiPE on microscopic molecular dynamics and macroscopic
video prediction tasks, achieving the best results on all nine multi-trajectory molecular evaluations and reducing KTH
video-prediction MSE by 9.9% relative to the best baseline while using roughly one-tenth as many parameters.
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All authors Yuhao Li, Louie Hong Yao, Tianyi Shi, Hanqun Cao, Hongxia Hao, Zhen Zhao, Shengchao Liu
Artificial Intelligence · Cross-listed from cs.LG
Xiangyang Wang, Bingxiang He, Zeyuan Liu et al. (+13 more)
Reward models underpin the
alignment of large
language models, yet the dominant designs reduce each
prompt–response pair to a point estimate or to a distribution from a fixed parametric family. This is at odds with human preference, which is inherently
multimodal: the same response can be reasonably judged in many ways, and no single family covers all of them. To better fit this structure, we introduce DRM, a
Diffusion Reward Model that recasts reward modeling as conditional density estimation over $p(\mathbf{r}\mid x,y)$. Conditioned on a frozen LLM encoder, a lightweight
Diffusion Transformer denoises Gaussian noise into a reward vector, placing no parametric assumption on the output distribution and naturally representing its
multimodal structure. A single architecture handles both multi-attribute regression and pairwise preference data, and at inference $N$ samples form an empirical reward distribution that can be aggregated into a scalar, a variance, or
quantiles. Across five
benchmarks, DRM matches or surpasses baselines under matched data and backbone, stays competitive with much larger discriminative, distributional, and generative RMs despite its modest training scale, and recovers
multimodal reward structure where conventional heads collapse to a point. Uncertainty-aware rejection and lower-confidence-bound (LCB) aggregation further demonstrate that DRM can exploit distributional information beyond a scalar reward to improve reward-model decisions. Downstream RLHF experiments additionally show that using DRM as the training-time reward leads to improved policy performance, directly validating the practical benefit of
diffusion-based reward modeling for RLHF training.
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All authors Xiangyang Wang, Bingxiang He, Zeyuan Liu, Jiaze Wang, Ziqing Qiao, Yuxin Zuo, Huan-ang Gao, Cheng Qian, Wenbin Zhang, Ran Li, Youbang Sun, Ning Ding, Yuanchun Shi, Zhiyuan Liu, Chaojun Xiao, Chun Yu
Artificial Intelligence · Cross-listed from cs.LG
Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?
Wenze Lin, Jiyuan Long, Jiale Zhao et al. (+11 more)
Since the advent of knowledge
distillation, KL divergence has been the standard loss in
distillation. Recently, on-policy
distillation (OPD) has emerged as an efficient post-training paradigm for LLMs. As a
distillation method, OPD naturally inherits KL divergence as its standard loss. However, in this work, we find that KL divergence may not be necessary for OPD. We show that simply preserving the update direction is sufficient for effective OPD. As long as the update direction is toward the teacher, OPD works. More precisely, it is not the direction of every
token, but the direction of a small subset of
tokens where the teacher and student disagree strongly. We first show that simply assigning a reward of (+1) to
tokens where the teacher probability is higher than the student probability and (-1) where it is lower, which merely encourages updates toward the teacher, reproduces almost the same training mode as OPD with reverse KL. We further show that only the direction of a small subset of
tokens with large teacher-student disagreement is critical, and training works as long as their update direction is toward the teacher, even if other
tokens are pulled away from the teacher. And as an application of these findings, we introduce Consensus Multi-Teacher On-Policy
Distillation (C-MOPD) to improve Multi-Teacher On-Policy
Distillation (MOPD). Unlike MOPD, which routes each sample to a single teacher and may cause capability conflicts across domains, C-MOPD lets every sample be supervised by all teachers. Experiments show that C-MOPD consistently outperforms MOPD on both math and code
benchmarks. Our code is available at https://github.com/LeapLabTHU/KL-Free-OPD.
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All authors Wenze Lin, Jiyuan Long, Jiale Zhao, Shenzhi Wang, Xitai Jiang, Ce Luo, Rui Lan, Qianli Ma, Fukang Wen, Hui Wu, Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Gao Huang
Artificial Intelligence
Is your uncertainty map wrong, or is its target? Exact diagnostics for the Tweedie diagonal, and a gradient-free alternative
Vicent Ribas and Anna Oliveras Tous
A
diffusion model can predict a follow-up medical scan from a baseline, but a clinician needs a per-voxel map of where that prediction can be trusted. Many such maps approximate the diagonal of the Tweedie posterior covariance, and are evaluated against another approximation of it, so whether the
estimator or the target limits them is unclear. We compute the exact diagonal on six checkpoints across fourteen model-corpus conditions. Hutchinson at M=200 tracks it at rank agreement of at least 0.92 everywhere, yet in four of the fourteen the exact diagonal is anti-correlated with the denoising error, reaching -0.13, so a faithful
estimator reproduces that reversal. All four are real-image conditions; on the models' own samples the reversal does not appear, so evaluating on generated samples flatters this family. What limits these maps is the target, not the
estimator. We then introduce Tweedie Probe-Tangent (T-PT), a gradient-free residual probe that corrupts one model-supported prediction repeatedly and measures the voxel-wise variance of the denoiser's response. T-PT reads a different functional of the same Jacobian, and its exact second-order form ranks with the diagonal wherever the diagonal reverses; at thirty probes it returns a map too unstable to reproduce that ranking, while Hutchinson at M=5 already reproduces it, so T-PT there is not evidence against the reversal. We offer it as an instrument, not a better approximation. On
brain MRI at full resolution, where every Jacobian-based
estimator we test runs out of
memory, T-PT leads a twenty-chain Monte-Carlo ensemble on five of eight endpoints inside tissue and trails it on none, at 16x fewer network evaluations; over the whole volume the ensemble leads, and fifty chains close the tissue gap. On lung CT the ensemble is ahead throughout. Both lose most of their discrimination where the change is, which remains open.
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Artificial Intelligence · Cross-listed from cs.LG
Surprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoning
Woongyeong Yeo, Minki Kang, Chanuk Lee et al. (+3 more)
Reinforcement learning with verifiable rewards (RLVR) enhances
reasoning in large
language models (LLMs) through outcome-level feedback, yet recent approaches to finer-grained credit assignment often require auxiliary models, additional sampling, or privileged information. Although policy
entropy provides a readily available signal, prioritizing uncertain positions under both reinforcement and penalization concentrates penalties where failed responses still retain alternatives for recovery, which can suppress opportunities for exploration. To address this, we introduce Entropic Advantage Policy Optimization (EAPO), an
entropy-guided credit assignment method that treats success and failure asymmetrically. Specifically, motivated by the observation that success under uncertainty is less repeatable while confident failures tend to recur, EAPO couples normalized policy
entropy with the sign of the response advantage to reinforce surprising success and correct repeated failure. It assigns stronger reinforcement to high-
entropy decisions in successful responses and stronger penalties to low-
entropy decisions in failed responses, while attenuating penalties at uncertain positions to preserve opportunities for recovery. By redistributing the response advantage across
tokens, EAPO derives
token-level credit directly from existing rollout signals without additional supervision. We validate EAPO on a range of
reasoning tasks across both base and
reasoning backbones, demonstrating that it achieves the best overall performance. We further show that EAPO promotes more effective exploration, broadening problem coverage and generating more diverse candidate answers.
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All authors Woongyeong Yeo, Minki Kang, Chanuk Lee, Sangwoo Park, Jinheon Baek, Sung Ju Hwang
Artificial Intelligence · Cross-listed from cs.LG
Selecting Diverse SFT Traces Improves Post-RL Generalization
Dylan Zhang, Mingyuan Wu and Jinning Li
Verified solutions are not equally useful for preparing
reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variation in the sequences of
reasoning steps in supervised fine-tuning (SFT) data, and propose a lightweight, rule-based fingerprint to select for it. From one pool at one budget, with matched training recipes and checkpoints, selecting diverse rather than similar routes improves post-RL problem coverage across puzzles and mathematics, including on problems harder than those seen in either training stage. In synthetic experiments, route-diverse SFT improves OLMo3-7B's pass@8 by 16.9 points on environments held out from SFT. In a single-model condition, where one model writes every candidate, diverse selection gains up to 6.2 points of mean pass@8 across 10 mathematics
benchmarks. Pre-RL diagnostics suggest why: diverse SFT can produce both successful and failed attempts on more
prompts despite slightly lower mean accuracy, giving group-relative RL more
prompts with a learning signal. On 3 open-source corpora, our CPU-only selector, without model calls, outperforms more expensive alternatives in every comparison of mean post-RL performance. These results identify
reasoning-route diversity as a practical criterion for selecting SFT data that better prepares models for RL.
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Artificial Intelligence
Evidence-Inference Reconstruction: When The Evidence Is Recalled But The Reasoning Goes Wrong
Megan Diehl and Ser-Nam Lim
Modern multi-hop LLM
agents are equipped with built-in mechanisms to detect errors in intermediate
reasoning steps. Such errors trigger corrective actions from these
agents, which mostly follow the paradigm of retrying the steps or the
reasoning trajectories. Not only are these retries expensive, we present in this paper that they are also potentially unnecessary. To this end, we introduce Evidence-Inference Reconstruction (EIR), which uses structured state to guide one
retrieval trajectory, accumulating source evidence in the process. We show that as long as the relevant evidence has been collected, EIR is capable of generating the correct answer in a single final model call even if erroneous evidence has been mixed in due to incorrect intermediate
reasoning steps. In one evaluation, using Haiku 4.5 and GPT-4.1 Mini, we evaluate EIR on matched 1,000-question subsets of HotpotQA, 2WikiMultiHopQA, and MuSiQue, showing that EIR improves Answer F1, the overlap between the model's and the correct answer, over the baseline by 8.3–32.8 points, Agentic SSR by 10.6–29.1 points, and Reflexion by 1.1–15.9 points. Additionally, we show that EIR averages 4.85 total model calls per question, compared with 35.29 for Agentic SSR and 12.41 for Reflexion. Together, these results corroborate EIR's central premise: separating evidence
retrieval from the final answer model call can improve answer accuracy while utilizing substantially less computation.
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Artificial Intelligence
Learning Strategies to Break Judges
Guruprerana Shabadi, Aaditya Naik, Rajeev Alur et al. (+1 more)
As AI
agents surpass human performance, it becomes exceedingly hard for system designers to evaluate them directly and understand their failure modes. Consequently,
agents themselves are being deployed extensively to evaluate, judge, and provide feedback on model traces. But this raises an important question: how can we trust the judge? In this work, we propose an
agent-guided method to find weaknesses of agentic judges that expose interpretable failure mechanisms. Our method focuses on mathematical
reasoning and proceeds in two stages: first, we deploy
adversarial agents to mutate a set of sound proofs by introducing errors, attempting to misguide judges—in other words, injecting errors that judges are unable to catch. Then, we distill these attempts into a small set of mutation strategies which allow us to analyze the failure modes of the judges. To ensure that these strategies are not overfit to the initial set of proofs, we evaluate them by applying the mutation strategies to a held-out set of proofs and querying the same judge. We deploy our method on GPT-5.6-sol and Claude Opus 5, paired with their
agent orchestrators, Codex and Claude Code, respectively. These are used both as mutators to introduce errors and as judges to evaluate correctness of mathematical
reasoning. We find that across all the agentic judges, we are able to distill mutation strategies that consistently bypass their evaluations, thereby enabling us to ascertain actionable failure modes. Our analysis also reveals that judge reliability degrades at the frontier: errors in Olympiad-level proofs or graduate-level mathematical texts are detected more consistently, whereas flaws in research-level manuscripts are more likely to escape detection.
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All authors Guruprerana Shabadi, Aaditya Naik, Rajeev Alur, Mayur Naik
Artificial Intelligence
Skill2Env: Capability-Oriented Environment Synthesis from Skills for General Agents
Weiyi Xu, Xiaowen Yang, Wen Da et al. (+7 more)
Executable environments are critical for post-training
agents on tasks that require tool use and multi-step interaction, but constructing executable tasks together with their environments remains difficult to scale. Skills provide reusable domain knowledge, operational procedures, and tool-use instructions, but a substantial gap remains between the information contained in a skill and a concrete, challenging task with a complete executable environment. To address this gap, we introduce Skill2Env, a capability-oriented framework that starts from a skill and uses
agent capability demands to guide task and environment synthesis. Skill2Env represents these demands through reusable difficulty patterns and instantiates them into task blueprints that specify objectives, challenges, environment facts, information boundaries, and acceptance criteria. These blueprints guide the joint construction of task instructions, execution substrates, workspaces, and rubric-based evaluators around source skills. We further propose Iterative Task Hardening, which uses solver execution evidence to identify insufficiently challenging task designs, strengthen or extend their difficulty-pattern instantiations, and revise the corresponding blueprints and environments. Using 1.5K high-scoring trajectories generated from Skill2Env environments for supervised fine-tuning, we observe consistent improvements across a broad range of
agent benchmarks, demonstrating the effectiveness of capability-oriented environment synthesis for
agent post-training.
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All authors Weiyi Xu, Xiaowen Yang, Wen Da, Hang Xu, Canwei Li, Hongjie You, Pusen Dong, Yucheng Zeng, Zhaokai Luo, Mu Chuan
Artificial Intelligence · Cross-listed from cs.LG
DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems
Jingjuan Huang, Wenbin Wang, Yanchuan Yin et al. (+2 more)
Multi-
agent systems (MAS) have recently emerged as an effective approach for coordinating large
language model (LLM)-based
agents to solve complex tasks through structured interactions. In practice, MASs often handle a stream of heterogeneous and complex tasks, requiring
agents to decompose each task and then self-organize and self-evolve to adapt to incoming tasks while sharing execution resources. However, most early approaches to MASs rely on centralized controllers or fixed coordination patterns, which can limit scalability or adaptability. In contrast, existing decentralized and dynamic MASs often require training dedicated routers or invoking LLMs for
agent selection, resulting in substantial computational costs and coordination overhead. To address these challenges and enable efficient task-level self-organization and self-
evolution for task- and workload-level collaboration, we propose Decentralized Expertise-Aware Load Serving (DEALS), a decentralized and low-complexity framework that enables
agents to self-organize and dynamically route concurrent tasks for processing. Specifically, each
agent maintains local queues of incoming tasks, and its router decides whether to process a task locally or forward it to a neighbor based on differences in backlog and success rate. Meanwhile, executors process independent tasks concurrently within and across
agents, and partially solved tasks can be resumed by other
agents. Experiments show that DEALS not only improves performance along multiple dimensions (e.g., answer accuracy and task throughput) in both homogeneous and heterogeneous
agent pools, but also balances
agent expertise and workload in a self-organized manner, enabling effective decentralized coordination.
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All authors Jingjuan Huang, Wenbin Wang, Yanchuan Yin, Alvaro Velasquez, Jia Liu
Artificial Intelligence · Cross-listed from cs.LG
Beyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily
Priyanath Maji, Sidharth Gaur and Rajavinoth Paul Durai
Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evaluations typically compare architectures under a fixed node-feature representation, leaving unclear whether conclusions about heterophily robustness remain stable as the input representation changes. We address this question by constructing parallel feature variants of two large-scale heterophilic
benchmarks, Roman-Empire and Amazon-Ratings, pairing each graph with representations ranging from static fastText vectors to contextual
Transformer embeddings and evaluating seven GNN architectures across these representations. We find that the effect of representation varies across architectures: on Roman-Empire, the contextual gain ranges from 2.38 percentage points for GCN-sep to 13.67 points for GAT, with H2GCN gaining 8.77 points. On Amazon-Ratings, where node text is limited to short product titles, GAT improves by 6.78 points from fastText to MPNet, while GCN-sep changes by only 0.20 points. These results show that architectural performance is conditional on node representation: the same representation change can produce different magnitudes of performance gain across architectures, so architecture and representation cannot be treated as independent evaluation factors. A rank-correlation analysis on these two
benchmarks further shows that the relative ordering of architectures remains highly stable across representations, isolating differential sensitivity, rather than ranking instability, as the primary effect.
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Artificial Intelligence · Cross-listed from cs.CR
SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
Xiaonan Luo, Yue Huang, Kehan Guo et al. (+9 more)
Assessing cybersecurity vulnerability awareness in coding
agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static
benchmarks offer fixed coverage and difficulty, while scarce vulnerable repositories and costly expert authoring limit their renewal at scale. We introduce SecProbe, a framework for adaptive evaluation that combines Item Response Theory (IRT) with on-demand synthesis of repository-scale vulnerability-repair tasks. From observed performance, SecProbe estimates
agent ability and identifies where additional evidence is most informative, selecting existing tasks or synthesizing new ones accordingly. As one use case, we construct 353 tasks spanning six programming
languages and 151 CWE types and evaluate nine frontier models with two
agent harnesses. Success rates peak at 28.33%, highlighting substantial gaps in vulnerability recognition and repair. Compared with random and one-shot baselines, SecProbe achieves comparable
agent ability estimates while requiring
agents to solve up to 29.5% fewer tasks. These results support adaptive evaluation as an efficient and discriminative approach to assessing cybersecurity vulnerability awareness.
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All authors Xiaonan Luo, Yue Huang, Kehan Guo, Ping He, Chuan Zou, Chujie Gao, Lichi Li, Yuchen Ma, Zhangchen Xu, Zichen Chen, Yufei Han, Xiangliang Zhang
Artificial Intelligence · Cross-listed from cs.SD
Transformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices
Luca Bompani, Marco Fariselli, Giovanni Oltrecolli et al. (+1 more)
Accurate, efficient, and low-latency spatial beamforming is a key component in emerging smart hearable devices, enhancing
speech while suppressing noise and interference. However, handling multiple input sources under strict real-time constraints poses significant challenges for the low-power, resource-constrained microcontroller units (MCUs) used in hearables. We present an optimized methodology for the real-time execution of a neural-network-based minimum variance distortionless response (MVDR) beamformer on MCUs. Using six microphones and a three-stage mixed-precision scheme (float32 MVDR, int8 CNN, float16
Transformer), the pipeline pairs a CNN that estimates the MVDR weights with a lightweight
Transformer that applies a per-frame correction. By time-slicing weight estimation with beamforming, it achieves a 15~ms per-frame latency while refreshing a complete set of CNN-derived weights every 564~ms. The deployed mixed-precision pipeline attains a short-time objective intelligibility (STOI) of 97.65%, a scale-invariant signal-to-noise ratio (SI-SNR) of 20.26~dB, and a wideband PESQ of 3.676 at an average power of 45.9~mW. A
speech activity detection (SAD) module (98.5% accuracy, 0.62~mJ per inference) bypasses the pipeline during silence; under realistic deployment conditions, the system exceeds the 16~h all-day target on a 100~mAh battery, with an estimated lifetime of up to $\sim$20~h. To our knowledge, this is the first real-time multi-channel
Transformer-based neural beamforming pipeline deployed on an MCU-class device.
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All authors Luca Bompani, Marco Fariselli, Giovanni Oltrecolli, Francesco Conti
Artificial Intelligence · Cross-listed from cs.LG
Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar et al. (+1 more)
Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by
privacy. This is acute in financial fraud detection, where rare fraud cases and imbalanced local
datasets limit decision-relevant analytics.
Federated learning enables collaboration without direct data sharing but does not resolve minority-class scarcity. Synthetic data generation can help, yet lightweight methods are interpolation-bound, while generative models require substantial data and computation. Existing collaborative generative approaches often rely on
federated learning, imposing considerable organization-side training burdens. In this paper, we examine CollaFuse as a collaborative
diffusion-based alternative for fraud detection and evaluate it across five fraud
datasets. Compared with classical oversampling, local generative baselines, and centralized
diffusion benchmarks, CollaFuse does not achieve the highest local fidelity but improves downstream fraud detection more consistently across most
datasets. These findings suggest that synthetic data create analytical value less through local realism than through transferable cross-organizational structure.
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All authors Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar, Niklas Kühl
Artificial Intelligence
HTN Planning as a Coordination Layer for Multi-Server MCP Tool Orchestration
Eliott Jacopin, Éric Jacopin and Koichi Takahashi
The Model Context Protocol (MCP) isolates servers by design: only the host can orchestrate cross-server workflows. When the host is a large
language model, the resulting orchestrations are non-deterministic, non-reproducible, and pay one inference round-trip per tool call. We present a coordination architecture in which a Hierarchical Task Network (HTN) planner generates a verifiable cross-server plan once, and a runtime middleware executes it deterministically across multiple MCP servers, binding cross-action data dependencies via a template mechanism (\verb|${context.X}|) substituted at execution time. The architecture mirrors MCP's isolation constraint: each compound task decomposes into server-local primitive actions, and inter-server data flow is bound at execution time via JSON-path output extractors. We instantiate the architecture on five HTN domains spanning laboratory robotics, bioinformatics and multiscale modelling, and demonstrate end-to-end execution from a browser-based plan controller against eight live third-party MCP servers querying real biological databases.
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Artificial Intelligence
Robust Biomolecular Complex Design Across Protein Conformational Landscapes
Qingyuan Zeng, Zongqi Xu, Anglin Liu et al. (+7 more)
Proteins populate conformational ensembles, yet structure-based biomolecular design typically optimizes candidates against a single target conformation. Consequently, a candidate that fits one state can lose favorable interactions or develop steric clashes when the target adopts another. We introduce FlexEvo, a model-agnostic evolutionary framework that adapts candidates once at inference time from a single target conformation to improve compatibility with alternative natural conformations unseen during adaptation, without retraining the source model or requiring a conformational ensemble. FlexEvo casts cross-state adaptation as geometry-constrained bi-objective optimization, balancing preservation of input-state interactions against robustness to plausible conformational perturbations. To limit the search space and reduce invalid structural edits, geometry-derived FlexBoxes define protected anchor regions, adaptable regions for local exploration, and forbidden regions for clash avoidance. A unified all-atom representation supports
topology-preserving adaptation across diverse binder categories, while Pareto selection preserves nondominated candidates across the two objectives. We evaluate FlexEvo across multiple generation baselines and nine representative binder categories spanning diverse molecular sizes and structural
topologies. FlexEvo reduces the category-balanced mean relative performance degradation from 47.8% to 4.4%, while adding only 1.4–3.1 minutes of adaptation per sample. These results establish single-state inference-time adaptation as a practical route toward robust biomolecular complex design across
protein conformational landscapes.
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All authors Qingyuan Zeng, Zongqi Xu, Anglin Liu, Ziqi Gong, Pengxiang Cai, Zixin Guan, Yunan Chen, Sen Gao, Min Zhou, Jintai Chen
Artificial Intelligence · Cross-listed from cs.LG
BOReFT: Manifold Steering of Language Models for Black-box Optimization
Dhruv Agarwal, Rico Angell, Kavitha Srinivas et al. (+4 more)
Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve proposals through iterative prompting or parameter updates, offering limited control over how completely and efficiently the model's search space is explored. Continuous optimization methods, such as
Bayesian optimization, provide a principled way to search but require a suitable domain to operate over. To address this, we introduce BOReFT, which learns a compact, low-dimensional space of hidden-state interventions in a frozen
language model, and uses this space as the search domain for
Bayesian optimization with an external scoring function. Empirically, we find that the learned domain spans semantic regions and exhibits smoothness properties that support search. Theoretically, we show that semantic coverage and interpolation control the best score available in the learned space, and that decoding from this space yields a standard
stochastic-bandit observation model for adaptive search. We evaluate BOReFT on the interpretable word search task "Semantle" and on three more real-world discovery tasks in de novo molecule property optimization. Compared to strong LLM baselines, BOReFT finds in Semantle a higher number of hidden targets and, on two out of three molecular objectives, achieves higher property scores. Consequently, our method provides a principled new bridge between discrete proposal spaces of LLM-based search and continuous black-box optimization.
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All authors Dhruv Agarwal, Rico Angell, Kavitha Srinivas, Tahira Naseem, Horst Samulowitz, Willie Neiswanger, Andrew McCallum
Artificial Intelligence · Cross-listed from cs.CL
From Granular Revision Operations to Meaningful Revision Units: Evaluating LLMs for Revision Boundary Detection
Yu Tian, Andrew Potter, Katerina Christhilf et al. (+4 more)
Revision traces provide valuable evidence about students' writing processes, but their usefulness for learning analytics depends on how individual revisions are represented. Automated draft-comparison methods often produce granular edit operations that can fragment a single purposeful revision into multiple analytic units. This study evaluates whether LLMs can identify meaningful revision unit boundaries in structured revision operation data and whether they provide value beyond simple non-LLM baselines. Using 113 matched draft–revision pairs from undergraduate writing, expert annotation yielded 4,344 candidate boundaries. We compared zero- and few-shot GPT-5.5 and base Qwen3-32B, parameter-efficient fine-tuning of Qwen3-32B, and majority and proximity-based baselines. Despite receiving revision context and task instructions, no prompted LLM condition outperformed the proximity heuristic (macro-F1 = .825). In contrast, fine-tuned Qwen3-32B using the two context representation achieved the highest macro-F1 (.859), identifying more same-unit relationships while maintaining precision comparable to the heuristic. Deterministic post-processing substantially improved the prompted models but added little benefit to the strongest fine-tuned model. These findings suggest that LLMs can support revision boundary judgment when task-adapted, but general purpose prompting alone may not outperform transparent structural heuristics.
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All authors Yu Tian, Andrew Potter, Katerina Christhilf, Motahareh Darvishpour Ahandani, Jessica Early, Steve Graham, Danielle S. McNamara
Artificial Intelligence
Self-Designed Evaluators and Warm Memory for Long-Horizon Agents
Saeid Asgari, Emre Kiciman, Leonardo de Oliveira Nunes et al. (+1 more)
A tool-using
language-model
agent deployed over a long stream of tasks receives no reward, so it cannot tell whether it succeeded, cannot safely retry, and cannot label the experience it needs to improve. We present SelfSuite, in which the
agent's own base model, given only the world's public materials, designs a small evaluation suite of weighted judges and grounded per-task briefs, freezes it, and uses it to gate a keep-best retry and to label a typed, outcome-tracked
memory. On matched five-repeat
benchmarks over tau2-bench and AppWorld, SelfSuite scores above the plain
agent without any labels, matches methods given ten expert labels on tau2-bench, and trails Agentic Context Engineering (ACE) on AppWorld, where code execution gives a direct success signal. In an ablation campaign run on the same tasks, it is above label-free ACE in every repeat, and the gated second attempt is the only component whose removal hurts in every repeat. We also simulate a subject-matter expert who grades ten onboarding tasks per world. Using those labels to calibrate SelfSuite's evaluator gives a small, consistent gain, and using them to warm up ACE's
memory lifts ACE to tie calibrated SelfSuite. A single-run study on a second model family shows the same ordering.
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All authors Saeid Asgari, Emre Kiciman, Leonardo de Oliveira Nunes, Ranveer Chandra
Artificial Intelligence
BIRD: Distilling Decision Boundaries into Rationales for MLLM Adaptation
Anglin Liu, Yanlin Wu, Ruichao Chen et al. (+6 more)
Adapting general-purpose
multimodal large
language models (MLLMs) to specialized domains requires learning domain-specific decision criteria, which often hinge on subtle visual distinctions between otherwise plausible answers. Rationale augmentation aims to expose such evidence through additional observations or inter-sample comparisons, yet a visually valid cue is not necessarily decision-relevant: it may describe how samples differ without changing the model's relative preference between competing answers. We therefore introduce BIRD, a self-improving Boundary-Informed Rationale
Distillation framework that uses model-specific confusions to locate unresolved local decision boundaries and distills the evidence that resolves these confusions into rationales. For each sample, BIRD retrieves candidate neighbors from the target MLLM's own representation space and selects the most confusable one according to its answer preferences. It then generates answer-blind candidate evidence from their visual differences and functionally verifies which evidence most effectively strengthens the model's preference for the correct answer while avoiding inappropriate transfer across the pair. The verified evidence is then distilled into a single-sample rationale for standard supervised fine-tuning. Experiments on medical and chart VQA show that BIRD outperforms competing rationale-augmentation methods across two target MLLMs, while further analyses demonstrate clearer separation of confusable answers and stronger gains from model-matched supervision.
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All authors Anglin Liu, Yanlin Wu, Ruichao Chen, Yuting Zhang, Qingyuan Zeng, Pengxiang Cai, Ziqi Gong, Muchen Li, Jintai Chen
Artificial Intelligence
Does Adversarial Training Improve Generalization in Multi-View VLAs? Revealing and Mitigating View Collapse
Futa Waseda, Shuhei Kurita and Isao Echizen
Vision-
language-action (VLA) models adapt pretrained
vision-
language models (VLMs) for closed-loop
robot control, transferring their perceptual and semantic capabilities to action prediction. Despite strong in-distribution performance, however, VLAs often degrade under deployment shifts.
Adversarial training (AT) offers a model-adaptive approach to robustness without explicitly anticipating individual shifts, but its effect on natural distribution-shift generalization in multi-view VLAs remains unclear. We study this question using a multi-view VLA directly adapted from a pretrained VLM and evaluate generalization across seven LIBERO-Plus shift axes. Direct AT substantially improves Camera Viewpoint and Sensor Noise, the two shifts affecting only the third-person view, yet produces mixed or negative effects on other shifts. Controlled view interventions reveal a surprising failure mode that we term view collapse: Direct AT can shift cross-view reliance so strongly that the policy becomes dominated by the wrist view. This exposes a robustness shortcut: apparent robustness to a shifted view can arise from reduced use of that view rather than more robust perception of it. This motivates a distinction between robust perception, extracting reliable information under within-view shifts, and robust fusion, adapting reliance across views according to their reliability. To reduce fixed view reliance, we use a simple View Swap intervention and then re-evaluate AT. With View Swap, AT further improves Camera Viewpoint, Sensor Noise, and
Robot Initial State, while its effects remain mixed on other shifts. Our results show that multi-view robustness requires separating improved perception from changes in cross-view reliance, and that AT provides selective rather than generic distribution-shift benefits.
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Artificial Intelligence · Cross-listed from cs.CL
Understanding Confabulation and Rethinking Reconstruction in Activation Explanations
Gert Lek, Zixuan Xia, Pin-Yu Chen et al. (+1 more)
Natural
Language Autoencoders (NLAs) produce unsupervised text explanations of a model's activations: a verbalizer describes an activation and a reconstructor learns to recover it from this text. Under the established point-reconstruction NLA training recipe, explanations become more useful for predicting model behavior while also increasingly introducing unsupported details and exhibiting writing defects. To assess these changes separately, we introduce a standardized evaluation framework for unstructured NLA explanations, measuring information recoverable from explanations, contextual support for their claims, and writing quality. To address confabulation and writing defects, we move beyond predicting a single activation: explanations can distinguish distributions of possible activations even when their means and optimal point-reconstruction rewards are identical. We introduce Flow-NLA, which models the distribution of activations compatible with an explanation and trains the verbalizer using a
diffusion likelihood bound. Across Qwen, Gemma, and Apertus, this richer signal retains the utility gains of point reconstruction while curbing the growth of confabulation and writing defects, opening up a direction for improving activation-derived training to encourage more informative, supported, and readable explanations. Code and evaluation
prompts will be made publicly available upon acceptance.
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All authors Gert Lek, Zixuan Xia, Pin-Yu Chen, Lydia Y. Chen
Artificial Intelligence
SpecRead: A Benchmark for Measuring Whether Language Models Understand Hardware Specifications
Feilian Huang
Existing
benchmarks for large
language models (LLMs) in hardware design evaluate downstream artifacts such as generated RTL, assertions, or testbenches. When a model fails such a
benchmark, the failure is ambiguous: it may have misread the specification, or it may have understood the specification and failed to write the code. We present SpecRead, a
benchmark that isolates specification comprehension from generation ability. SpecRead v2.1 contains 385 questions over 10 open-source OpenTitan IP blocks: exact
retrieval, cross-section
reasoning, contradiction detection in mutated specifications, and spec-RTL consistency checking, plus 82 controls (41 distractor, 41 consistent-RTL). Type-4 items are built from real RTL mutations; we retain only mutations that Icarus Verilog simulation shows to change observable behavior. A with-spec vs. without-spec ablation suggests the questions require the excerpt, not training recall alone (without-spec accuracy 3/20 on the t1/t2 subset), though memorization of the source text may still help spot mutations. As an initial characterization with a small model, Ministral-3B scores 33.2% overall (128/385; macro average 39.0%): 55.2% on
retrieval, 51.7% on cross-section
reasoning. On the two contradiction-focused types, the verdict-plus-location measure gives 48.0% (t3) and 63.3% (t4), with a 51.2% false-positive rate on distractors and 100% on consistent-RTL controls. Layered scoring shows the model locates contradictions well (78.9-81.6% location accuracy) but scores lower on their category (43.9-49.7%). A structured "rule-table" prompting intervention lowers accuracy on every question type except t2 (tied). SpecRead is automatically scorable by deterministic checks, with gray-zone cases counted wrong under the conservative main scoring. The
benchmark is regenerable for type-3 items via mutation injection, and built exclusively from public sources.
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Artificial Intelligence
Yulin Yuan, Ying Zhang and Xiangming Meng
Most continuous
diffusion language models process one latent position per
token at each sampling step, making generation expensive. Two-stage methods lower the cost by reducing the latent length, but they fix the compressed
embedding space before training the
diffusion model.
Embeddings from the fixed space can be difficult to model with
diffusion and decode reliably into
tokens, which limits generation quality after compression. To address this problem, we introduce JPEG-DLM (Joint-
embedding Prediction for Efficient Generation with
Diffusion Language Model), which jointly trains a compressor, a flow matching model and a decoding module. With joint-
embedding prediction, JPEG-DLM learns compressed
embeddings that are more structured, easier to model with
diffusion and reliably decodable into
tokens. JPEG-DLM achieves the lowest mean Gen-PPL and highest throughput among recent
diffusion and flow models on LM1B and OWT. At a compression rate of 0.5 on OWT, it reaches a Gen-PPL of 34.52 and approximately 2.3 times ELF's throughput. These results suggest that jointly learning compressed
embeddings offers a promising path toward efficient
diffusion language modeling. Code will be released soon.
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Artificial Intelligence · Cross-listed from cs.LG
Dynamic Kuramoto-Hodge Operators for PDEs on Complex Geometries and Topologies
Xiang Li and Yue Song
Learning PDE operators on complex domains requires capturing interactions among fields on vertices, edges, and faces, alongside global responses shaped by
topology. Existing neural operators accommodate irregular geometries but often overlook these distinct field supports or their condition-dependent coupling. We introduce the Dynamic Kuramoto–Hodge Operator (DKHO), which combines
topology-constrained interactions with learned coordination. DKHO encodes conditions on their native cochain supports, evolves Kuramoto-inspired relation states through the boundary and coboundary operators that compose the Dirac operator, and decodes non-harmonic and harmonic responses in orthogonal Hodge subspaces.
Topology thus determines where information can flow, while learned dynamics adapts how it is exchanged to each PDE instance. Across porous-medium Darcy flow,
torus transport–
diffusion, and cavity magnetostatics, DKHO-large reduces prediction error by approximately 61% on average over leading baselines, while DKHO-small remains competitive using only 11.5–24.3% as many parameters. These results suggest that coupling topological structure with adaptive dynamics provides an effective inductive bias for accurate and parameter-efficient PDE operator learning on complex geometries and
topologies.
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