Artificial Intelligence
Evidence-First Agentic Verification
Reza Sanayei, Razvan-Gabriel Dumitru, Sunjie Hou et al. (+7 more)
Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state and the execution trace. A judge may overlook this evidence or accept unsupported claims, including an
agent's own reports of success. When rubric verdicts serve as reinforcement-learning rewards, such errors can reinforce undesirable behavior. We introduce Sauron, an evidence-first agentic verifier that investigates both state and trace and relies on a claim only after independently validating it against its source. Bounded queries and joint investigation of related criteria reduce context use and redundant work, and each verdict carries an auditable receipt of its supporting evidence. Across 2,586 human-labeled criteria from 100 personal-assistant episodes, Sauron achieves the highest F1 of five verifiers on all eight backbones tested while costing less than Gandalf, the strongest baseline. With GPT-5.6 Sol, it reaches 0.96 F1 versus Gandalf's 0.92 at 23% lower cost, with a wider margin on process criteria. On a paired fault-injection suite covering 13 families, several of which simulate reward hacking, Sauron detects 97% of injected failures while flagging only 2% of clean controls, outperforming baselines even when they are given the full trace. On TRAIL, a
benchmark for detecting and localizing real
agent errors, Sauron leads both Gandalf and TRAIL's own judge on all three quality metrics.
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All authors Reza Sanayei, Razvan-Gabriel Dumitru, Sunjie Hou, MohammadHossein Rezaei, Haris Riaz, Robert Vacareanu, Minglai Yang, Ying Liu, Mihai Surdeanu, Daniel Yue Zhang
Artificial Intelligence
Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains
Chun-Yu Hsu, Lun-Wei Ku and Chung-Chi Chen
People act on evaluative judgments produced by LLM
agents, and in multi-
agent systems those judgments pass through several
agents before reaching a person. We show that a workflow's information architecture—source access, handoff format, and which model reads the untrusted content—governs how much of a coordinated manipulation reaches the end. On 32 Taiwan~2025 recall cases, ten authentic Threads comments shown to one
agent move its stance by $+0.171$ toward the injected position, and a mirrored set moves it the other way. Passing that judgment down a chain removes more than half of the shift by the third
agent; keeping the comments in front of every
agent sustains more of it. The reduction is not attrition in transit: each receiver discounts the inherited stance in proportion to how far it sits from its own reading of the evidence, returning to what that evidence implies for the case rather than to neutrality—consistent with partial re-anchoring. Handoff format is a second lever: a stance relayed without its
reasoning is largely discounted. Susceptibility at the exposed seat varies fourfold across models, while the chain's arrangement does not matter at all. None of this is visible in the transcripts—the injected claims are almost never restated, so
reasoning-trace audits miss the attack.
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Machine Learning · Cross-listed from quant-ph
Representation Geometry and Inductive Biases in Quantum Classifiers across Classical Visual Benchmarks
Yu Yamaoka, Weng Ian Chan and Kosuke Mitarai
Quantum Machine Learning (QML) has a theoretical advantage over classical
algorithms for specific synthetic data that is computationally hard. However, it remains unclear whether such theoretical advantages play a meaningful role for realistic classical data, such as natural images.
Quantum structural properties induced by
unitary operations or
entanglement introduce inductive biases that differ from those of classical models, potentially leading to distinct learning behavior on real-world data. In a fully
quantum learning pipeline, classical real-world data are first encoded into
quantum states via a
quantum feature mapping, analogous to a classical feature extractor, and a
quantum classifier then classifies the resulting states.
Quantum feature mappings generate representations that are difficult to realize classically, while also inducing representation-level biases that do not necessarily match the structure of realistic data. To enable a fair comparison at the classifier level, we focus on the classifier and use classical feature extractors that already provide powerful representations for natural images. By controlling the data scale and supervision strength, we analyze how the
unitary structure of
quantum classifiers affects generalization, and show that their effectiveness depends strongly on the geometry of the input representations. These results indicate that
quantum classifiers benefit from classical features that are aligned with
quantum feature spaces. Our analysis shows that kernel
alignment identifies representations compatible with
quantum feature spaces, providing insights into translating classical models into
quantum algorithms.
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Artificial Intelligence
Keep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs
Vishisht Choudhary
Agents build knowledge graphs as
memory, and building them has become easy. Maintaining them has not. When a source is retracted, overruled, or erased, everything built on top of it (entity merges, entailed edges, community summaries) must be re-examined, and the maintainer must choose between deleting too much and too little. Deployed pipelines delete only the directly extracted facts: on our corpus that reduces contaminated queries from 32.2% to 28.0% under a real retraction and changes nothing under predicate revocation. Rebuilding is no repair for a constructor that samples: two builds of the same 300-document corpus differ in 29,475 artifacts, five times the 6,248 that removing the 61 retracted documents changes. PolyGraph instead asks the constructor for receipts: for every artifact, alternative bundles of source evidence, each claimed sufficient on its own, emitted in the call that creates it. Cleanup is then set arithmetic over the receipts, with no model calls. Because receipts are the model's own self-report, a re-derivation audit checks them by deleting the cited evidence and testing whether the constructor still produces the artifact. The audit finds that a receipt is trustworthy when it is shaped like the claim it backs. Merge receipts, each backing one narrow claim, agree with re-derivation 68% and 85% of the time. Summary receipts, elicited per sentence but stored as one flat list for the whole text, are wrong about two thirds of the time, almost always by keeping dead content. One receipt per sentence instead is 100% and 88% faithful and lets cleanup keep 33 of 35 touched summaries. The result holds under a second oracle model, a claim-blind
prompt, ten further deletions, a second domain and a second constructor. Distrusting the summary layer cuts contaminated queries to 0.4% and 0.8% at 99.8 to 100% of the recall a perfect maintainer would return.
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Artificial Intelligence
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng et al. (+8 more)
Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a recursive self-improvement framework combining verified waveform synthesis with feedback-guided intervention search. Signal remeasurement verifies waveform edits; construction errors guide generator repair, while learner errors motivate interventions compared on a separate selection set. Across seven
benchmarks, three rounds raise Qwen3.5-35B-A3B's mean question-answering accuracy from 34.57% to 56.04% and mean classification F1 from 24.27 to 34.03. Across the retained trajectory, the preferred supervision shifts from direct reinforcement to confusion-focused contrasts. Continued feedback correction yields diminishing external gains. These results motivate separating error diagnosis from intervention selection and checking whether gains on the feedback set transfer to independent
benchmarks.
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All authors Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Jiangwei Lao, Quanbin Wang, Honglin Lin, Jiajia Liu, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu
Data Structures and Algorithms · Cross-listed from math.PR
Sampling from the Sherrington-Kirkpatrick Model up to Criticality
Hang Du, Brice Huang, Sidhanth Mohanty et al. (+1 more)
We prove that the simulated annealing
algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature $\beta < 1$ to an arbitrary inverse
polynomial accuracy in total variation.
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All authors Hang Du, Brice Huang, Sidhanth Mohanty, Amit Rajaraman
Sound
Multimodal Contrastive Representation Learning Based Voice Conversion for Electrolaryngeal Speech Enhancement
Ding Ma, Jinyi Mi, Jiajun He et al. (+3 more)
Electrolaryngeal (EL)
speech suffers from severe acoustic mismatches with normal
speech, resulting in degraded naturalness and intelligibility. These mismatches also make sequence-to-sequence voice conversion struggle to learn robust intermediate representations, hindering EL-
speech-to-normal-
speech conversion (EL2SP) performance. To address this, we propose a novel
multimodal contrastive representation learning framework to 1) achieve richer
multimodal-representation learning, and 2) facilitate the transferability of
multimodal representations to
speech-only EL2SP. First, EL
speech and text representations are fused to learn
multimodal representations for target
speech generation, during which cross-modal contrastive learning is employed to encourage a semantically consistent
speech–text representation space. Next, the fused representations are treated as a fixed target to guide
speech-only representation learning through cross-stage contrastive learning, enabling
multimodal knowledge to be transferred to the EL2SP task while preserving architectural and inference simplicity. Experimental results demonstrate that the proposed method consistently outperforms both the
speech-only and
multimodal-based baselines.
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All authors Ding Ma, Jinyi Mi, Jiajun He, Kazuhiro Kobayashi, Naoki Nishio, Tomoki Toda
Machine Learning
MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series
Genpei Zhang
Flow matching has emerged as a powerful alternative to
diffusion models for generative modeling, but standard conditional flow matching is fundamentally misaligned with partially observed time series: starting from an uninformed Gaussian source forces the model to transport even already-observed entries from noise to the target, wasting capacity on trivially known regions. We propose MissPath-FM, a partial-observation-aware flow matching framework that redesigns the source prior and probability path for incomplete time series. MissPath-FM employs structured source priors (interpolation priors and Gaussian process posterior priors) to initialize the flow close to the observed signal, paired with a missingness-aware smooth path that allocates transport asymmetrically between observed and missing regions. We evaluate systematically across four
datasets, four missingness mechanisms (MCAR, block, MNAR, irregular), two missing ratios, and three sequence lengths (T in {24, 48, 96}), totaling 48 settings against eight baselines. MissPath-FM wins 29 of 32 settings at T=48 and 45 of 48 overall (93.8%). Our ablations reveal a regime-dependent design hierarchy in which no single component suffices: under random missingness the source prior yields a large initial gain, but under structured block missingness it can fail entirely and the learned flow becomes indispensable; the missingness-aware path contributes a further 12%-26% across all regimes. Mechanistically, structured priors shorten observed-region transport by up to 15x, concentrating the velocity field on genuinely uncertain entries. Project page: https://zgp110.github.io/misspath-fm/
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Machine Learning
Confine to the Task, Anchor the Rest: Concept-Guided Retention Regularizer for Parameter-Efficient Fine-Tuning
Ibna Kowsar, Ivan Medri, Fanyu Meng et al. (+1 more)
Parameter-efficient fine-tuning (PEFT) has been widely used, yet the adapted model suffers catastrophic forgetting of pre-trained knowledge. While most existing work hardens the adapter by constraining where the weight update may lie, we note that constraining this weight channel does not constrain the activation channel. In this paper, we propose Concept-Guided Retention (CGR), a plug-and-play regularizer that constrains both for any PEFT base. CGR reads a frozen sparse autoencoder (SAE) and treats two disjoint concept sets differently. It softly confines the update to the task-concept subspace (via weight channel), and anchors a selected set of general-purpose concepts to the frozen model (via activation channel). We also provide anchor value bounds based on weight updates to understand how they interact, and show that both terms are necessary. On a CLIP
vision panel, CGR significantly improves retention at comparable accuracy for every base, and the same pattern holds for world-knowledge retention under Llama-3.1-8B instruction tuning. Because the protected directions are named SAE concepts, CGR remains inspectable and controllable.
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All authors Ibna Kowsar, Ivan Medri, Fanyu Meng, Xin Liu
Data Structures and Algorithms
Fully Dynamic Random-Walk Quantities on Undirected Expanders and $\kappa$-Uniform Directed Graphs
Richard Peng, Sushant Sachdeva, Junzhao Yang et al. (+1 more)
We study fully dynamic data structures for random-walk quantities on undirected expanders and fast-mixing directed graphs. For an unweighted graph whose normalized
Laplacian has spectral gap at least $\gamma$, we give a $(1\pm\varepsilon)$ effective-resistance data structure with expected update time $O(\gamma^{-4}\varepsilon^{-2}\log n\log^4(n/\varepsilon))$ and expected $O(1)$ query time. For directed graphs, somewhat surprisingly, the OMv
conjecture precludes a fast data structure based on rapid mixing conditions alone. To address this, we introduce the notion of $\kappa$-uniform directed graphs, which requires the stationary distribution to $\kappa$-approximate the uniform distribution, and show that in this setting, random walk based data structures with $\text{poly}(\kappa, \log{n}, \varepsilon^{-1})$ update/query times are possible.
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All authors Richard Peng, Sushant Sachdeva, Junzhao Yang, Yibin Zhao
Cryptography and Security
Local Leakage Resilience of Shamir Secret Sharing: Below Half Threshold Rate
Hai H. Nguyen
We study the local leakage resilience of Shamir secret sharing, where an adversary leaks a bounded number of bits from every share. For $n$ parties and reconstruction threshold $k$ over a
prime field $\mathbb{F}_p$ with $p>n$, we prove the following. (1) There is an absolute constant $c_{\mathrm{bh}}<1/2$ such that, for $k\ge c_{\mathrm{bh}}n$, Shamir sharing is resilient to arbitrary one-bit leakage from every share. The best previous threshold was $0.668n$. (2) For every fixed $0<\delta<1/2$ and $k\ge(1/2+\delta)n$, Shamir sharing is resilient to $\left\lfloor\frac{\delta}{24}\log_2 n\right\rfloor$ bits of arbitrary local leakage per share. By a recent attack, this number of bits is optimal up to a constant factor depending on $\delta$. (3) For every fixed threshold rate $c>0$ and $k\ge cn$, Shamir sharing is resilient to quadratic-residue leakage and to almost all local leakage functions of up to $\eta_c\log_2 p$ bits, for a constant $\eta_c>0$. All three results follow from one analytic framework, which we develop for generalized Reed–Solomon codes: local leakage from a uniform codeword is statistically close to the same leakage from independent uniform field elements. The framework expresses the leakage distance as the norm of a function on the leakage outputs. The Efron–Stein decomposition splits this function into orthogonal components indexed by supports of dual codewords. Two tools bound the components: list recovery of the dual code, which is ordinary list decoding for one-bit leakage, and a generalized von Neumann inequality for MDS systems of linear forms that retains linearly many Gowers norms. The first tool gives the result above half, the second gives the constant-rate result, and applying both to different ranges of supports in the same decomposition gives the result below half.
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Artificial Intelligence
Adaptive-Group Process Rewards for GUI Agents
Zirui Shang, Yang Liu, Xin Shu et al. (+3 more)
Training graphical user interface (GUI)
agents with reinforcement learning suffers from sparse outcome rewards, leading to inefficient learning. Process rewards offer a promising direction for credit assignment to intermediate actions; however, accurately evaluating an action requires identifying evidence across long-horizon GUI trajectories, as its value may depend on information beyond its immediate state and observation. In this paper, we propose an adaptive-group process reward framework that dynamically partitions a GUI trajectory into semantically coherent subgoal groups, enabling process reward estimation at both the action and group levels. At the action level, a critic evaluates each action based on the visual changes and subsequent actions within the group for action correctness and contribution to the subgoal. At the group level, the framework aggregates action-level evaluations to assess subgoal completion and contribution to the overall task, and further refines these assessments by leveraging the context of all groups in the trajectory. The process reward of each action is obtained by combining its action-level evaluation with the refined group-level evaluation. We further augment GRPO's outcome advantage with the proposed process rewards to provide finer-grained guidance for GUI
agent training. Experiments on process reward
benchmarks and downstream RL training demonstrate the effectiveness of the proposed framework.
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All authors Zirui Shang, Yang Liu, Xin Shu, Zhi Gao, Xinxiao Wu, Lifeng Fan
Machine Learning
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya et al. (+1 more)
Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the
dataset. Yet not all states are created equal: in a small number of critical states, even a slight deviation from the expert action can lead to a failure, while in many other states such errors are recoverable and inconsequential. We formalize state criticality in imitation learning as the worst-case degradation in downstream value induced by a bounded perturbation of the expert action at a given state. We then propose SCRIM, a practical State CRIticality Measure that estimates this quantity by training an
adversarial disturbance policy and a value critic around a fixed imitation policy. SCRIM assigns each demonstration state a score reflecting how vulnerable the task outcome is to small, policy-relevant action errors. We show that these scores can be used to reweight behavior-cloning losses, improving imitation at consequential states and increasing overall task performance. We further show that SCRIM enables data curation by selecting compact, high-value subsets of demonstrations that preserve performance while substantially reducing the amount of training data. We evaluate SCRIM across various simulated and real-world robotic manipulation tasks, and demonstrate that SCRIM improves policy training, enables efficient data selection, and provides interpretable insights into which moments in a demonstration are most critical for task success. Project website: https://sia-lab-git.github.io/SCRIM/
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All authors Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya, Somil Bansal
Programming Languages · Cross-listed from quant-ph
QEDIA: Quantum Program Verification in Lean
Yuanjie Ren, Zhicheng Zhang, Junyi Liu et al. (+3 more)
Practical
quantum programs interleave
quantum and classical computation: classical data shape the
quantum operations that are performed, and measurement outcomes shape the classical computation that follows. Formal verification of such programs in a proof assistant requires mechanizing a program logic that handles this interplay consistently at every level, from the mathematics of
quantum mechanics, through program syntax and semantics, to specifications and proof rules. Such mechanizations, along with the program proofs built on them, typically require substantial manual effort. We present QEDIA, a framework for verifying
quantum programs with classical variables in Lean 4. At its foundation is an open library that formalizes the
quantum Hoare logic QHL+ across all these levels and proves the logic sound for both partial and total correctness. We construct the library and the program proofs, under human supervision, using an LLM-based prover
agent, whose general-purpose core we extend for
quantum program verification. We illustrate QEDIA with verified programs including the
quantum Fourier transform, phase estimation, repeat-until-success
circuits, and
Hamiltonian simulation.
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All authors Yuanjie Ren, Zhicheng Zhang, Junyi Liu, Jinzheng Li, Yidi Qi, Mingsheng Ying
Machine Learning
Focus on What You Need: Global-to-Local Dynamic Attention for Multimodal Sarcasm Detection
Dehao Wu and Xingjie Zhuang
Multimodal sarcasm detection has attracted increasing
attention due to the widespread use of images and text on social media platforms. Existing studies generally treat the semantic or emotional inconsistency between images and text as a key cue for identifying sarcastic expressions. However, images in real-world scenarios often contain multiple visual objects, many of which are irrelevant to sarcasm-related semantics. Such redundant visual information may introduce noise during cross-modal interaction, distracting models from focusing on critical sarcasm cues and potentially leading to inaccurate predictions. To address this issue, we propose GLDA (Global-to-Local Dynamic
Attention), a
multimodal sarcasm detection framework that models cross-modal interactions from a global-to-local perspective. Specifically, GLDA first employs a global
multimodal fusion module to capture high-level semantic interactions between visual and textual modalities, enabling the model to identify coarse-grained cross-modal inconsistencies. To further reduce the influence of irrelevant objects, GLDA introduces a local gated cross-modal
attention mechanism that dynamically filters redundant visual and textual features and refines
multimodal representations at a finer granularity. In addition, a multi-branch learning objective is designed to jointly optimize unimodal and
multimodal representations. Experimental results demonstrate that GLDA effectively improves sarcasm detection performance and achieves competitive results compared with existing approaches. The code will be available at https://anonymous.4open.science/r/GLDA-DBE6.
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Computer Vision
From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models
Hyeonggeun Han, Taehyun Cho and Sangwoo Hong
Large
vision-
language models often exhibit object
hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-Guided Visual Claim Admission (EGVCA), a training-free framework that admits a semantic claim only when it receives sufficient image-dependent support. EGVCA measures this support by comparing the claim likelihood under the observed image and a visual-null input. For captioning, it controls semantic expansion without altering
token generation. For binary visual question answering, it combines atomic visual support with the image-induced change in the model's native Yes–No margin. Experiments on CHAIR, POPE, and MME across three LVLM backbones show that EGVCA substantially reduces object
hallucination while preserving caption quality. It achieves the highest POPE F1 in eight of nine backbone–sampling settings and improves the native MME total score on all three backbones. These results demonstrate the effectiveness of claim-level admission as a simple alternative to
token-level distribution correction.
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Computer Vision
A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
U-Chae Jun, Yoonhyuk Choi and Jiwoo Kang
3D scene graph prediction is commonly supervised with local object and predicate classification losses. Although effective for slot-wise label prediction, this formulation does not provide a graph-level semantic distance between a predicted scene graph and its ground truth: semantically mild label errors and structurally disruptive relational errors can be treated similarly, and subject-predicate-object facts are not compared as scene-level units. We propose Contextual Hellinger Triplet Geometry, a graph-level supervision objective that measures the semantic and structural discrepancy between 3D scene graphs. Our key idea is to reinterpret the conventional classifier outputs of a 3D scene graph predictor as an aligned probabilistic scene graph field, where object and predicate predictions jointly define contextual subject-predicate-object facts. This formulation yields a differentiable metric-based training objective with an efficient factorized implementation and can be applied to existing 3D scene graph prediction architectures without modifying their network design. Experiments on 3DSSG demonstrate that our supervision improves downstream graph-conditioned 3D scene generation, while ablation studies confirm the complementary roles of the proposed graph-level terms and user studies show better graph descriptiveness, relation plausibility, and structural preservation.
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Computation and Language
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively
Li Lucy, Myra Cheng, Svenja Guhr et al. (+7 more)
The sycophancy of
language models, or their failure to push back against faulty user input, is now widely studied. However, if LMs do push back, is sycophancy solved? We show that LMs vary in how they present pushback, and thus vary in how effectively they correct user beliefs. We introduce a phrase-level, human-validated response labeling framework to uncover fine-grained insights into how LMs push against and/or affirm false inputs. We develop and apply our framework on model responses in five
languages: English, German, Chinese, Hindi, and Yoruba. We find that when models do push back against users' false assertions, they tend to use indirect phrasing and mix in affirmation, with more indirectness in Hindi and Yoruba than in other
languages considered. In a large-scale human study (n = 300), we find that the pushback ambiguity we identified may fail to correct and prevent users' factual misconceptions.
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All authors Li Lucy, Myra Cheng, Svenja Guhr, Kabir Ahuja, Orevaoghene Ahia, Sameep Chattopadhyay, Yike Wang, Valentin Hofmann, Noah A. Smith, Yulia Tsvetkov
Artificial Intelligence
A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining
Mostafa Abbasi and Homayoun Najjaran
Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conformance metrics such as fitness and precision, which are highly sensitive to noise and deviations in event logs and therefore often fail to give a consistent assessment of discovery
algorithms. We introduce a model-centric evaluation framework that combines node
embeddings with Procrustes
alignment to enable cross-graph comparability, since
embeddings derived from different graphs reside in distinct vector spaces. The proposed PetriNetGraph method transforms Petri net process models into graph representations compatible with
embedding algorithms. Comparing the node
embeddings of ground-truth and discovered models through a parameterized cosine distance yields a unified and bounded metric for evaluating process discovery across
datasets and methods. We evaluate the framework under static and context-aware scenarios to examine how prior knowledge of activity existence influences the assessment of structural similarity. The framework also integrates a compliance checking mechanism that localizes deviations at the activity level and an
entropy-regularized node distance aggregation function that quantifies global deviations across the model. Experiments on nine publicly available event logs show that the approach provides a reliable
embedding-based conformance indicator that reflects the behavior of fitness and precision. Experiments on synthetic logs with controlled noise levels confirm that it is substantially more robust to log deviations than traditional conformance metrics, PetriNet2Vec, and its dynamic-window and Procrustes-aligned variants, while remaining sensitive to process model degradation. All implementation details and
datasets are publicly available.
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Numerical Analysis
Semi-Lagrangian Approximation of Master Equations with Individual Noise on Finite Graphs
Jianbo Cui and Tonghe Dang
We develop and analyze a fully discrete semi-Lagrangian method for vector master equations associated with mean field games (MFGs) with individual noise on finite graphs. The method discretizes both the characteristic
evolution in time and the population variable on the probability simplex. A central challenge is the degeneracy of the transport coefficients near the boundary of the open simplex, where no boundary condition is prescribed. To overcome this difficulty in the analysis, we introduce an interior localization based on exact density characteristics, which keeps numerical arrivals within compact regions of uniform coefficient control. Combined with a closure argument for the coupled numerical recursion, this localization yields local solvability of the updates and quantitative error bounds on compact subsets of the open simplex. Under additional $C^2$-regularity of the exact solution and suitable space–time mesh coupling, these bounds imply first-order accuracy. Furthermore, the density and value trajectories generated by the numerical master feedback converge to the corresponding MFG characteristics at the same rate. In the potential setting, the associated reconstructed density–flux pair is admissible and asymptotically optimal for the planning problem, with an action gap quadratic in the feedback error. Numerical experiments illustrate the convergence rates, feedback responses to population redistribution, and the effects of graph
topology.
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Artificial Intelligence
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Xiaoqing Lian, Pei Liu, Jiahui Tan et al. (+3 more)
Single-
cell RNA sequencing (scRNA-seq) enables
cell-level analysis of biological heterogeneity, but learning effective representations from high-dimensional, unordered gene expression profiles remains challenging. Gene Expression-to-Image (GE2I) offers an effective learning strategy by arranging gene values on pixel grids for convolutional neural networks, but its underlying mechanisms remain unclear. We study GE2I on full-transcriptome data while keeping the network and training procedure fixed. Our results reveal a trade-off between spatial smoothness and gene-level fidelity. Structured mappings produce smooth, low-frequency patterns that support faster early optimization, whereas assigning multiple genes to the same pixel obscures expression differences and can reduce predictive performance. This trade-off explains why random mappings can compete with structured layouts when many genes are represented. One-to-one gene-to-pixel assignment improves prediction across the tested visual backbones even when image smoothness decreases. We further find that the benefits of pretraining and augmentation depend on the backbone and mapping. These findings provide practical guidance for pixel allocation and the transfer of
vision priors to gene expression data.
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All authors Xiaoqing Lian, Pei Liu, Jiahui Tan, Quan Zou, Chen Lin, Xiangxiang Zeng
Machine Learning
LeanPRM: Lean-Grounded Process Supervision for Natural Language Proof Verification
Rodney Lafuente-Mercado, Salman Rahman, Jingyan Shen et al. (+3 more)
AI systems now write long and difficult mathematical proofs, and checking their step-by-step validity is becoming a bottleneck. A learned verifier could check proofs cheaply, but training one requires a correctness label on every step, and such labels are hard to obtain for open-ended proofs. Existing methods take them from human annotators, which is costly, or derive them from final answers, which proofs do not have, and formalizing each proof in Lean requires expert effort. We introduce LeanPRM, a framework that uses the Lean proof assistant to create step-level labels for natural-
language proofs. A model writes a natural-
language proof together with a paired formal proof, without feedback from Lean. Lean then labels every step, and a second model discards failures caused only by the formal syntax, so that the negative labels mark errors in the mathematics. LeanPRM contains 10,634 proofs with 152K labeled steps and no human annotation. A Qwen3-4B verifier trained on LeanPRM raises exact step-label accuracy on a held-out set of 200 proofs (LeanPRM-Bench) from 48.2 to 74.7, as opposed to 60.3 when the same base model is trained on PRM800K. It also selects better proofs in best-of-N selection on IMO-level problems, and outperforms the PRM800K-trained verifier on three of four external verification
benchmarks. Training on LeanPRM improves verifiers at every size from 0.6B to 8B parameters on LeanPRM-Bench, Hard2Verify and IMO-GradingBench. Given the labels need no human annotation, LeanPRM can grow with the supply of formal statements, offering a path to step-level feedback on AI-generated proofs that are hard for humans to check.
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All authors Rodney Lafuente-Mercado, Salman Rahman, Jingyan Shen, James Shiffer, Saadia Gabriel, Pavel Izmailov
Machine Learning
zPocket: Better Pocket Finding with Frozen Co-Folding Features
Tally Portnoi, Allison M Keys, Benjamin R DiFrancesco et al. (+1 more)
Identifying
protein-ligand binding sites (pockets) is important for understanding
protein function and developing drugs. However, no public method has leveraged co-folding models for novel pocket prediction; furthermore, this task lacks a suitable
benchmark as existing
benchmarks are within the training distribution of co-folding models. We therefore developed PocketWatch,a curated
dataset of biologically relevant ligand pockets. The PocketWatch test set consists of unseen pockets stratified by sequence similarity, structural similarity, and crypticity to enable evaluation of generalization to novel and challenging pockets. Using this
dataset, we develop zPocket, a method that trains pocket-prediction heads on top of frozen features from co-folding models. We show that zPocket heads outperform baseline methods across all stratifications. Our best zPocket model achieves overall per-target AUPRC of 0.745 compared to 0.598 and 0.595 for the strongest baselines, VN-EGNN and UniSite-1D retrained on the PocketWatch split (0.559 and 0.531 when using the published weights); the widely used P2Rank reaches 0.477. We further show that zPocket can be used to outperform baseline methods on top-N+2 recall of predicted pocket centroids through clustering per-residue predictions or using zPocket to rescore centroids proposed by other models. Finally, we explore two applications of zPocket: crystallographic fragment screens and a proteome-scale scan for uncharacterized pockets on human
proteins. Together these results suggest that zPocket heads built on co-folding trunk features can greatly advance ligand binding site prediction.
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All authors Tally Portnoi, Allison M Keys, Benjamin R DiFrancesco, Nate Gruver
Robotics
HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution
Haoran Wang, Jingwei Huang, Anqing Duan et al. (+2 more)
We introduce HelpMeOut, an embodied AI
benchmark for error-aware monitoring of household task execution. Existing embodied household
benchmarks typically assume that humans execute tasks correctly, but this assumption does not hold for cognitively impaired persons or older adults whose everyday activities produce structured execution errors. To address this gap, HelpMeOut operationalizes research on everyday action errors into 20000 paired activity episodes, covering nine neuropsychologically motivated error categories. In each episode,
agents observe a streaming human activity trajectory and must monitor whether the execution is unfolding correctly, diagnosing any structured errors as early as possible. We evaluate learned-monitor, symbolic, change-point, and frontier LLM prompting baselines. Results show that these methods perform poorly as error monitors, often missing when execution goes wrong. These results suggest a gap left by standard goal-inference and task-completion
benchmarks, making HelpMeOut a testbed for error-aware assistive
agents.
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All authors Haoran Wang, Jingwei Huang, Anqing Duan, Ho Seok Ahn, Bruce Macdonald
Computer Vision
SWORD: Scene-Grounded Wasserstein Object-Relation Distance for 3D Scene Graph Prediction
U-Chae Jun, Jaeeun Ko, Yoonhyuk Choi et al. (+1 more)
3D scene graph prediction is commonly optimized with object-node and relation-edge classification losses. These objectives encourage exact object and relation label recovery, but they do not directly optimize semantic proximity to the ground-truth scene graph: two wrong graphs can receive comparable penalties even when one is much closer in object roles and relation contexts. A direct graph-distance loss is difficult in general, since graph matching and graph edit distance require correspondence search and edit-cost design. Our key observation is that prior supervised 3D scene graph prediction protocols are conducted on fixed object-node indices and supervised edge slots. This can turn a scene graph comparison into a well-defined aligned graph-distance problem. To this end, we propose SWORD, a Scene-Grounded Wasserstein Object-Relation Distance that builds block-normalized object and relation ground distance metrics from training scene graph annotations through a deterministic base-to-context order, without external semantic priors. SWORD lifts these metrics to Wasserstein distances over aligned node and null-augmented edge marginals, providing semantic graph-distance supervision beyond exact-label penalties while preserving the ground-truth graph as the unique zero-distance solution in aligned marginal probability space. We prove that SWORD is a strict metric on aligned marginal probabilistic scene graphs, preserves scene-graph-grounded substitution ordering, controls additive graph-grounded semantic observables, and yields a surrogate upper-bounding decoded semantic error.
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All authors U-Chae Jun, Jaeeun Ko, Yoonhyuk Choi, Jiwoo Kang
Artificial Intelligence
MedEvo: Medical Reasoning Evolution through Knowledge Navigation
Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng et al. (+10 more)
Medical
multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid
reasoning in students' own failed trajectories, while general-domain correction methods offer little guidance for errors in image reading, anatomical context, or differential diagnosis. We propose MedEvo, which uses medical knowledge, namely a tag taxonomy and error-type diagnosis, to navigate both trajectory repair and data selection. For each failed rollout, a gold-blind auditor locates the first consequential error and provides a minimal, answer-free hint; the same student retains the valid prefix and regenerates the erroneous suffix. After each round, MedEvo identifies capability gaps across medical tags on a held-out feedback set and prioritizes related questions for new rollouts and repairs. With supervised fine-tuning (SFT) alone, MedEvo raises Qwen3.5-9B mean accuracy across seven
benchmarks from 65.57% to 69.12% (47.40% to 54.50% on the
multimodal track of MedXpertQA (MedXpertQA-MM)), surpassing teacher-
distillation SFT (66.93%). It also raises Qwen3.5-35B and Qwen3.5-397B mean accuracy from 67.91% to 73.04% and from 71.83% to 74.95%, respectively. Our analyses show that failed trajectories contain
reasoning worth preserving, that repair success does not imply learning value, and that independent feedback is key to sustained improvement.
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All authors Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Quanbin Wang, Jiangwei Lao, Yingao Liu, Jiajia Liu, Jinming Cao, Honglin Lin, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu
Artificial Intelligence
Stable Token-level Scheduling for Serverless LLM Inference
Hardi Ashishbhai Desai, Balaji N K and Sourav Kanti Addya
Serverless inference is emerging as an efficient deployment model for Large
Language Models (LLMs) due to its ability to elastically scale resources, improve GPU utilization through resource sharing, and reduce infrastructure costs. However, existing approaches primarily optimize time to first
token (TTFT) while overlooking inter-
token latency (ITL), a key determinant of user experience during response generation. We observe that LLM inference comprises a computation-intensive prefill stage and a bandwidth-bound decode stage, where admitting new requests can disrupt ongoing decoding and increase latency. To address this, we propose
Token Window Pacing (TWP), a closed-loop scheduling framework that leverages runtime feedback from ITL, queue depth, and workload characteristics to regulate request admission at the
token level instead of the batch level. TWP dynamically adjusts a selfregulating
token window and incorporates chunk-based prefill to minimize interference from incoming requests. Experiments on realistic serverless workloads show that TWP delivers stable TTFT, significantly reduces latency spikes, lowers P99 tail latency, minimizes ITL variation, and improves throughput by up to 45% over ServerlessLLM and 58% over conventional schedulers. These results demonstrate that feedback-driven tokenlevel scheduling enables stable, high-throughput serverless LLM inference without modifying the underlying model.
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Robotics
OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching
Qihang Huang, Wenhui Huang, Collister Chua et al. (+6 more)
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving. However, existing occupancy-aware planners typically couple occupancy prediction and action generation through either hierarchical coupling or parallel multi-task learning. Hierarchical methods use occupancy either as a fully predicted future preceding action generation or as an action-conditioned rollout for trajectory evaluation, whereas parallel multi-task methods predict occupancy alongside planning through a separate branch or auxiliary objective. These interfaces retain either an explicit generation order or a role asymmetry, limiting reciprocal interaction between world prediction and action generation during trajectory formation. We propose OccWAM, an occupancy-native world-action model that jointly generates dense future occupancy and continuous ego trajectories via flow matching conditioned on scene representations. Building on this joint formulation, we introduce scene-to-ego world-action flow matching, a two-stage formulation. The first stage predicts scene-centric occupancy together with an absolute ego trajectory, providing a stable representation of future scene
evolution. The same predicted world is then re-indexed into the future ego frames induced by the initial trajectory, where ego motion is represented through adjacent pose increments and a second joint flow refines ego-centric occupancy and motion for action-aligned planning. Unlike
video-based world-action models, OccWAM preserves explicit semantic geometry for direct planning
reasoning without pixel synthesis. Extensive experiments on Bench2Drive and nuScenes demonstrate state-of-the-art performance in closed-loop driving, open-loop planning, and future occupancy prediction.
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All authors Qihang Huang, Wenhui Huang, Collister Chua, Shiyu Fang, Zhan Chen, Songyan Zhang, Zhidong Wang, Daiying Tian, Chen Lv
Computation and Language
Encoding Context into LLM Weights: What Makes It Context?
Yue Wang, Qizhou Wang, Feng Liu et al. (+3 more)
Longer contexts allow large
language models (LLMs) to condition their predictions on more information, but inference costs grow prohibitively with context length. An emerging alternative uses test-time updates to encode context as temporary changes to model weights. However, encoding this information into weights that already represent general knowledge obscures its role as context, so the LLM may fail to use it properly to condition subsequent inference. To restore this contextual role, we first associate each context with an identifier during encoding, thereby distinguishing the newly encoded information from pre-existing knowledge. Invoking the identifier at inference then enables the LLM to recite the encoded context. Nevertheless, successful recitation does not ensure that the LLM will use the recalled information as context. We therefore further introduce a prompting framework that explicitly guides the LLM to use this information as context. Across diverse controlled tasks and practical
benchmarks, our method outperformed the baseline using only off-the-shelf LLMs, showing its advances in context encoding.
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All authors Yue Wang, Qizhou Wang, Feng Liu, Gang Niu, Masashi Sugiyama, Bo Han
Computation and Language
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents
Jingyue Cong, Estrid He, Zhuang Li et al. (+5 more)
Enterprise
agents face safety risks that cannot be determined from individual actions alone. Existing safety
benchmarks largely evaluate action-local risks, overlooking failures whose safety depends on workflow state. In such state-dependent risks, the same action may be safe in one context but violate policy in another. Hence, we introduce LatentRiskBench for evaluating such state-dependent risks in enterprise settings. It pairs risky trajectories with benign counterparts that share the same terminal action but differ in latent policy-relevant states, requiring models to reconstruct workflow context rather than rely on action-level cues. We also introduce new metrics for state-dependent risk evaluation, including a suite of residual risk (RR) measures. Experiments on the
benchmark show that action-level evaluation detects only 0.09 of state-dependent violations, whereas state-level evaluation reaches 0.96. Existing guardrail methods largely overlook such risks unless they are explicitly applied to workflow states. Case studies on a commercial deployed scenario further show that the safety metrics embedded in the
benchmark provide a more realistic measure of safety for enterprise
agents. Our
dataset and code are available at https://github.com/JingyueCong/LatentRiskBench.
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All authors Jingyue Cong, Estrid He, Zhuang Li, Nan Wu, Li Shen, Yameng Peng, Xinshu Li, Andy Song