Artificial Intelligence
Positive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening
Thilo Tamme, Michael Saatkamp, Alma Bonte et al. (+3 more)
Organizations started listening to employees through conversational AI
agents alongside structured surveys. Little is known about what these channels change in what employees say when disclosure carries hierarchical risk. We report a field study inside a global management consulting firm whose process pairs a pre-survey with an adaptive AI voice interview on the same themes within one session. Across 44 first-session interviews (132 matched theme observations), 20-41% of sessions showed a favorable rating co-occurring with a substantive concern voiced later, depending on the favorability threshold. The Gioia analysis drew on 158 protective quotes from 65 eligible sessions. Disclosure rarely arrived unguarded: employees softened concerns, deflected accountability, and bounded how far they went, and this protective work tracked the perceived legitimacy of the listening structure. We develop a grounded model of bounded disclosure and derive four propositions for voice, channel and listening research. Silence, we argue, can persist inside expression.
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All authors Thilo Tamme, Michael Saatkamp, Alma Bonte, Daniel Weiss, Anton Hantel, Andrej Levin
Machine Learning
Same Loss, Different Gradients
Ningkang Peng, Xiaoqian Peng, Yifan He et al. (+4 more)
Differentiable learning typically assumes that the scalar objective evaluated in the forward pass and the gradient supplied to the optimizer in the backward pass describe the same mathematical object. We show that this correspondence can fail when probabilistic objectives rely on finite special-function recurrences, custom backward rules, and numerical clipping. In high-dimensional von Mises-Fisher learning, real numerical implementations can produce identical forward scores and losses at the same learning state while supplying different gradients and following different optimization trajectories. We characterize the structure of this mismatch in finite-start Bessel recurrence and show that classwise radial mismatch can compose through probabilities into a locally nonconservative update field. Evaluating the accuracy of special-function values and derivatives separately is therefore insufficient to characterize the realized learning objective. Motivated by this observation, we introduce AR/FR, a fixed-depth analytic realization that constructs a potential and its derivative jointly, ensuring forward-backward coherence by construction. We establish a uniform cubic-order error bound relative to the exact Bessel ratio over the entire nonnegative concentration axis and propagate this guarantee to learning scores and objectives. As representation dimension increases, the original finite recurrence becomes sequentially deeper, whereas the worst-case AR/FR error guarantee tightens cubically, jointly providing coherence, certified fidelity, and fixed-depth computation. These results suggest that a differentiable numerical primitive is defined by both the values it realizes and the derivatives it actually supplies to the optimizer; together, they constitute the numerical realization of the learning
algorithm.
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All authors Ningkang Peng, Xiaoqian Peng, Yifan He, Anjie Hu, Chao Tan, Peirong Ma, Yanhui Gu
Machine Learning
How Accurate Is Accurate Enough?
Ningkang Peng, Qianfeng Yu, Jingyang Mao et al. (+2 more)
How accurate must a numerical approximation be within a learning system? Primitive error alone cannot answer this question: errors of the same magnitude can have very different consequences for losses, predictions, and gradients at different learning states. We study this question through the learning objective itself. The objective weights classwise numerical errors nonuniformly according to the current state, so the importance of an error depends not only on its magnitude but also on the class it affects and the weight that class receives. For softmax cross-
entropy, we characterize this coupling between class weights and errors and derive the exact extrema of the signed loss change over pairings of fixed non-target probability and score-error multisets, with the target probability and target score error held fixed. Building on this structure, we establish finite-error guarantees that propagate primitive error to losses, probabilities, predictions, and feature gradients, then invert these guarantees to obtain a certified primitive tolerance for the current state under prescribed learning-level error requirements. We give a complete instantiation of the framework in high-dimensional von Mises-Fisher learning. Controlled interventions and a large collection of saved learning states show that identical primitive error can produce substantially different learning consequences, while certified numerical tolerances vary by orders of magnitude across states under the same learning-level requirements. These results show that the adequacy of a numerical approximation must be assessed in relation to the current learning state and the quantity to be preserved; numerical accuracy should itself be treated as part of the learning objective.
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All authors Ningkang Peng, Qianfeng Yu, Jingyang Mao, Xiaoqian Peng, Yanhui Gu
Machine Learning
Does a Shared Temperature Imply a Shared Angular Scale in Probabilistic Contrastive Learning?
Ningkang Peng, Qianfeng Yu, Jingyang Mao et al. (+4 more)
In probabilistic contrastive learning, a shared temperature is commonly interpreted as a shared similarity scale, but this interpretation does not hold for high-dimensional distributional class representations. We study the exact von Mises-Fisher (vMF) probabilistic score used by ProCo when representation dimension and class concentration grow jointly. We prove that the score retains a class-dependent leading angular gain $g_c=A_c/\tau$, where $A_c$ is the mean resultant length. This gain enters Softmax competition, pairwise decision boundaries, and feature gradients. On real CIFAR-LT, ImageNet-LT, and iNaturalist representations, the theory accurately predicts boundary movements and local gradient changes under the full vMF score. Classwise temperature adjustment also changes the cosine-zero intercept and finite-dimensional response. We construct intercept-preserving and Pure Angular controls to separate the leading gain from these accompanying changes. Complete gain equalization yields a shared-scale cosine prototype rule at leading order; a finite-dimensional margin condition guarantees agreement of the two classifiers. Across 16 frozen representation settings, prediction agreement is 98.43-99.99%, with disagreements concentrated at small cosine margins. In controlled contrastive-only training with the training-frequency prior, Pure Angular editing improves both learned representations at all tested CIFAR-10/100 imbalance factors and retains positive changes on ImageNet-LT. Thus vMF concentration not only describes class distributions, but also forms a decision and learning scale in high-dimensional probabilistic contrastive learning.
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All authors Ningkang Peng, Qianfeng Yu, Jingyang Mao, Xiaoqian Peng, Tingyu Lu, Peirong Ma, Yanhui Gu
Artificial Intelligence
Persona and Persuasive Framing in AI Voice Agents: A $2\times2$ Field Experiment with Children
Thilo Tamme, David Steck and Anton Hantel
Conversational
agents increasingly interact with children, yet evidence on how their design shapes children's susceptibility to persuasion comes almost entirely from the lab. We report a $2\times2$ randomized field experiment embedded in a public German Santa Claus telephone hotline. Children's calls were randomly routed to one of four LLM voice
agents varying persona (Santa, high authority, vs. Helper, low authority) and framing (persuasive nudges toward prosocial wishes vs. neutral). Of 1,072 logged calls, 89 conversations (median age 6) met inclusion criteria. Persuasive framing raised the probability of a prosocial wish from 11.6% to 45.7%, robust to controls. Persona authority showed a near-zero effect: Santa did not outperform the Helper. Persona instead shaped engagement; children hung up on the Helper far more often within the first minute (65% vs. 39%). Where context already lends an
agent legitimacy, how it speaks shapes children's compliance more than who it claims to be.
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Machine Learning
ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density
Xinhe Wu and Yadong Jin
Multimodal large
language models (MLLMs) offer a promising approach for recovering numerical data from scientific charts, but their ability to reconstruct chart data from visually dense figures remains poorly understood. Existing chart understanding
benchmarks primarily evaluate question answering or chart-level
reasoning and provide limited support for evaluating structured numerical reconstruction from scientific figures. We introduce ChartDensity-Bench, a
benchmark for evaluating MLLMs on structured numerical data reconstruction from compound chart figures under controlled visual density. Built from charts paired with source-level ground-truth data, ChartDensity-Bench systematically varies the number of simultaneously presented charts ($k\in{1,3,6,9}$), enabling controlled evaluation of density-induced degradation. We further propose a multi-dimensional evaluation framework covering structural reliability, reconstruction completeness, parseability, and numerical fidelity. Experiments on five recent MLLMs show that numerical reconstruction generally degrades as visual density increases, while the magnitude of degradation varies substantially across models. Chart-level paired comparisons further show that the same source chart can incur higher reconstruction error when embedded in denser visual contexts. These findings highlight visual density as an important and previously underexplored factor in MLLM chart data reconstruction and provide a systematic
benchmark for evaluating model robustness in this setting.
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Sound
RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition
Ji Hwan Park, Gautham Krishna Gudur, Yufei Shen et al. (+2 more)
Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in everyday environments. High-resolution (HR) audio provides rich acoustic information for model development but incurs substantial energy and storage costs and may expose sensitive
speech content. Low-resolution (LR) audio offers a more
privacy-preserving and resource-efficient alternative for deployment, but reduced sampling rates can remove acoustic cues essential for activity recognition, leading to significant performance degradation. We formulate this training-deployment mismatch as sensor-resolution privileged learning, in which HR audio is available during training, while inference relies exclusively on LR audio. We propose RAST, a resolution-aware transfer framework that compresses HR teacher representations by preserving
token-level information and neighborhood structure before performing localized HR-LR
alignment. Experiments on the SAMoSA and AudioIMU
datasets show that RAST consistently outperforms LR-only training and direct teacher-transfer baselines, improving LR-only recognition by up to approximately 7.8% while requiring only LR audio at inference.
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All authors Ji Hwan Park, Gautham Krishna Gudur, Yufei Shen, Dawei Liang, Edison Thomaz
Emerging Technologies · Cross-listed from quant-ph
Motzkin-Straus Optimization on an Entropy-Computing Platform
PoJen Wang, Sutapa Samanta, Yuntai Song et al. (+1 more)
We introduce a framework for combinatorial optimization using sum-constrained continuous quadratic programs solvable by QCi's Dirac-3S photonic
entropy computer. This is enabled by the Motzkin-Straus
theorem which provides a powerful bridge between discrete clique problems and optimization over the probability simplex. We demonstrate this framework's versatility by solving constraint satisfaction problems, providing extensive
benchmarks on the DIMACS suite. The Dirac-3S platform matches or outright leads two independently implemented classical baselines on more than four-fifths of the
benchmark instances, reaching the best known solution on nearly all structured graph families, even outperforming both classical solvers on several of the largest instances tested. On the other hand, well-tuned classical continuous optimizers retain an edge only on the hardest planted-clique instances. This work establishes a viable pathway for solving combinatorial optimization problems using natively analog unconventional computing platforms, while positioning
entropy computing as a competitive approach for navigating non-convex landscapes and providing rigorous baselines for an emerging computational paradigm.
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All authors PoJen Wang, Sutapa Samanta, Yuntai Song, Mohammad-Ali Miri
Artificial Intelligence
Action Conditioned Bisimulation For GUI Agent Memory
Hongbo Zhang, Liuyang Song, Quanquan Li et al. (+3 more)
An
agent that remembers what it did on a web page must decide when two pages count as the same.
Memories built on observation similarity merge pages that look alike but behave differently, and GUIs are full of such pages: two tabs of one widget or two rows of one menu answer the same click differently. We define the merge rule as an action-conditioned bisimulation over the empirical predictive state graph a frozen
agent fills as it acts. Two states merge only when their shared actions lead to agreeing outcomes and successor blocks under an affordance label. Observation similarity never enters the rule, and nothing is trained. It replaces the merge rule of an existing outcome-value
memory, so a closed-loop comparison isolates it. On MiniWoB++ it raises success rate over a memoryless
agent, while a control taking identical exploratory detours, the prior successor-representation merge, and the same criterion without action conditioning change nothing.
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All authors Hongbo Zhang, Liuyang Song, Quanquan Li, Daqian Yang, Yan Wen, Zhengtao Yao
Computer Vision
Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models
Yuanhao Sun, Huawei Ji, Jiaxin Ding et al. (+2 more)
A central goal of
vision-
language model (VLM)
distillation is to transfer both the teacher's
language capabilities and its visual understanding. However, existing methods primarily supervise the student's output, leaving visual understanding implicit. Our analysis reveals that a student can match the teacher's answer without relying on the same visual evidence, raising the question: how can we ensure the student responds to the visual information that actually determines the answer? To this end, we propose Cross-World On-Policy
Distillation (CW-OPD), which explicitly supervises the student's response to changes in visual evidence. For each example, CW-OPD constructs two visual worlds that share the question and scene context but differ in answer-critical evidence, yielding different answers. We perform on-policy
distillation in both worlds and distill the teacher's cross-world belief transition, encouraging the student to match not only what the teacher predicts but also why its prediction changes with the evidence. A gradient analysis shows that this term is invariant to errors shared by both worlds and supplies a corrective signal invisible to endpoint matching alone. In this way, CW-OPD makes reliance on the relevant visual evidence an explicit
distillation target rather than an implicit consequence of output matching. To diagnose whether a model truly grounds its answers in visual evidence, we introduce CWBench, which measures cross-world consistency via Cross-World Pair Accuracy (CWPA). Experiments on Qwen3.5-4B show that CW-OPD outperforms the strongest baseline by 1.2 points on average, and the 4B student exceeds DeepSeek-V4.1 (552B) by 22.4 CWPA points on CWBench. Code is released in https://github.com/baokou-fw2/CWAD.
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All authors Yuanhao Sun, Huawei Ji, Jiaxin Ding, Luoyi Fu, Xinbing Wang
Machine Learning
Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
Shixuan Liu, Joan Serrà, Kin Wai Cheuk et al. (+6 more)
Training data attribution for
diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample gradient computation or query-specific model optimization. Moreover, most methods attribute changes in a proxy loss rather than changes in the actual model's generative behavior. We address these limitations by formulating attribution directly with a local score discrepancy measure, which applies to any
diffusion variant (including DDPM, EDM, and flow matching), and by showing that such measure can be estimated without retraining, as a preconditioned gradient similarity. We instantiate this
estimator as Training-data Influence via score Discrepancy (TID), which uses Kronecker-factored
curvature to avoid random projections and per-sample gradient storage. We then distill TID into TIDE, a forward-only student trained online to reproduce the teacher's rankings from the
diffusion model's internal activations. Under counterfactual evaluation on CIFAR-10, ArtBench-10, and MS-COCO, TID matches or outperforms state-of-the-art approaches, while TIDE retains most of TID's accuracy at four to five orders of magnitude lower per-query cost, attributing generated samples in milliseconds and faster than the generation itself.
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All authors Shixuan Liu, Joan Serrà, Kin Wai Cheuk, Jinju Kim, Woosung Choi, Yukara Ikemiya, Wei-Hsiang Liao, Jiaqi W. Ma, Yuki Mitsufuji
Computational Complexity · Cross-listed from quant-ph
Experimentally Testable Quantum Advantage in Shallow Circuits
Kishor Bharti and Adán Cabello
Experimental tests of shallow-
circuit quantum advantage require explicit classical bounds at finite
circuit sizes. We refine the finite-size classical soundness bound of Aasnaess's graph-distributed construction to depend linearly on the number of players. Combined with standard disjoint-player repetition, this gives a two-round test on a single processor for any finite nonlocal game with a finite-dimensional perfect
quantum strategy and classical winning probability $\gamma<1$, with arbitrarily small classical success. The method is based on playing $m$ copies with disjoint players and teleporting each player's register to a uniformly random one of $N$ sites before the questions are revealed. Each answer bit of a depth-$D$, fan-in-$K$ classical response with fixed wiring depends on at most $K^D$ question wires, making cross-player dependencies unlikely when $N$ is large. The
quantum implementation has constant depth per round and wins with certainty. A classical device wins with probability at most $\gamma^m+O(mK^D/N)$, which vanishes as $O(\log N/N)$ for $m=\lceil\log_{1/\gamma}N\rceil$ at fixed $D$ and $K$. We present an explicit proposal for an experimentally testable
quantum advantage with 99
qubits.
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Machine Learning
Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics
Fujie Gao, Zuyue Zhang and Gang Sun
Prior work has shown that neural networks exhibit implicit biases toward low-complexity structure (e.g., spectral bias), memorization dynamics, and compression-like effects during training, but a unified dynamical account of selective retention remains incomplete. We propose Repeated Reinforcement with Persistent Forgetting (RPF) dynamics, a minimal framework in which repeated exposure reinforces patterns and structures that recur in the data, while persistent forgetting attenuates learned information. This view treats forgetting not merely as a failure mode, but as a selection mechanism. We build the theory in three successive layers. First, in an independent-feature model, we derive an exposure-selective survival law and a support-dependent retention boundary characterizing which patterns persist under forgetting. Second, in a shared-parameter model, we show that forgetting induces spectral filtering over covariance modes, preserving strongly supported shared components while suppressing weak ones. Third, under small-step and norm/coding approximations, we show how RPF dynamics induce an implicit trade-off between data fitting and the cost of stored information, yielding Minimum Description Length (MDL)-like compression. Controlled experiments provide evidence for this reinforcement–forgetting selection mechanism in scalar
memories and a nonlinear shared network. Joint reinforcement and attenuation interventions shift conditional retention, while matched exposure counts reveal forgetting-dependent effects of reinforcement timing and changes in the composition of the retained set. Together, these results show that repeated reinforcement and persistent forgetting jointly provide a controllable source of inductive bias beyond neural architecture and scale.
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Machine Learning
dattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale
Shixuan Liu, Tongli Zhou, Junwei Deng et al. (+2 more)
Training data attribution (TDA) estimates the contribution of individual training examples to model outputs. Most scalable TDA methods rely on per-example gradients, whose computation and use at LLM scale pose challenges in efficiency, compatibility, and extensibility. We introduce dattri-LLM, a TDA library that makes gradient-based attribution more practical at scale. For efficiency, dattri-LLM uses compact gradient representations and dynamically routes gradient operations based on a cost model. For compatibility, its capture mechanism collects per-example gradients from existing training loops that call backward(), without requiring changes to the loop or its configuration. This includes distributed training with DDP and FSDP and pipelines built with HuggingFace
Transformers, TRL, and OLMo. For extensibility, dattri-LLM exposes reusable gradient operations and training-time callbacks for implementing attribution methods and applications. These interfaces support a variety of attribution methods, including gradient similarity,
curvature-based influence, and trajectory-based methods, as well as applications that act on gradients during training, such as online data selection. On the same hardware and workload, dattri-LLM achieves 3.2x the throughput of the fastest competing library on average, scales multiple attribution methods to 110B-parameter models across four H200 GPUs, and offers superior attribution fidelity-cost trade-offs across a range of models with different model families and scales. The source code of dattri-LLM is available at https://github.com/TRAIS-Lab/dattri-llm.
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All authors Shixuan Liu, Tongli Zhou, Junwei Deng, Pingbang Hu, Jiaqi W. Ma
Artificial Intelligence
PathAnchor: Path-Structured Evidence for Scientific Agents
Qiuhui Chen, Jiafan Lu, Shuaimin Tang et al. (+5 more)
Scientific
agents can retrieve relevant passages yet still lose functional order, mix evidence across sources, or state conclusions that exceed the retrieved record. We introduce PathAnchor, a bounded scientific
reasoning system built on path-structured evidence workspaces. Instead of treating passages or extracted concepts as independent units, the system retrieves source-linked Material-Sensor-Signal-System trajectories that preserve role, direction, and the evidence supporting each transition. A controller uses three read-only tools to search paper-specific trajectories, trace paths across candidate sources, and open exact evidence before producing a claim-cited answer and an explicit evidence boundary. On 120 single- and cross-paper flexible-sensor questions, PathAnchor scores 82.6% and leads six evaluated systems. Under a matched controller, corpus, and six-call budget, replacing unordered concept graphs with path-structured records raises source recall from 61.3% to 82.9%, increases answers whose claims all cite opened evidence from 69.2% to 90.0%, and reduces tool calls. These results show that evidence organization affects
retrieval and citation completeness under fixed
agent resources.
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All authors Qiuhui Chen, Jiafan Lu, Shuaimin Tang, Tao Dai, Suyuan Wang, Chenrui Ji, Zhenglei Zhou, Weimin Zhong
Machine Learning
Lasting Effects of Abstract Pretraining Beyond Perplexity
Zachary Shinnick, Hemanth Saratchandran, Damien Teney et al. (+1 more)
Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract, algorithmically generated data can provide a better starting point for subsequent learning of natural
language. In this paper, we show that in small
language models, such a warm-up improves specific capabilities that are not reflected in
language-modeling perplexity. Our warm-up uses an abstract stack-manipulation task that requires compositional and state-tracking capabilities. Allocating as little as 1% of pretraining
tokens to this data improves multi-hop question answering by up to 3.9 F1 points on MUSIQUE, with additional gains on HOTPOTQA and 2WIKIMULTIHOPQA despite comparable
language-modeling perplexity. Controlled experiments show that the warm-up substantially accelerates the acquisition of deeper
reasoning chains. We also explore what drives this transfer. First, the structure of the data matters: replacing the stack task with a queue fails to produce the same gains. Second, the gains are specific: performance improves on sequential
reasoning chains, with no consistent benefit on tasks that combine or compare independent facts. Third, timing matters: mixing abstract data with natural
language is far less effective than an initial dedicated phase, and exposure after pretraining completely removes the benefits. The early advantage persists through billions of subsequent
language tokens. These results show that early abstract training can reliably shape the capabilities
language models later acquire.
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All authors Zachary Shinnick, Hemanth Saratchandran, Damien Teney, Anton van den Hengel
Software Engineering
Adaptive-GEPA: Make Your Harness Fit Heterogeneous Requests
Tianyu Chen, Yasi Zhang, Ruiyi Wang et al. (+3 more)
Reflective optimizers such as GEPA improve
language model
prompts from execution traces and evaluator feedback; full-program extensions can also rewrite tools and control flow. In practice, a user hands the same endpoint heterogeneous requests whose effective solutions require different tools,
reasoning modes, and control flow. Optimizing one shared program leaves this division of work implicit in source-code search, while optimizing a separate program per request family fixes it beforehand. We introduce Adaptive-GEPA, which learns both how to divide requests and how to solve them. It evolves a router and a library of specialist programs under one search budget. The router's instructions, each specialist's description, and its program code are plain, human-readable text, edited from feedback. To combine branches, it aligns specialists by the requests they handle and inherits descriptions together with programs. On a fixed mixture of four task families, the reported Qwen3-8B run evolves four experts without supplying family labels to the router or reflection model; its routing matches the task partition on all 651 test requests. Its family-mean test score (x100) rises from 52.6 to 70.6, compared with 62.5 for GEPA's full-program adapter and 54.0 for GRPO at a nominal budget of 18,000 scored calls. These counts do not equate total compute. Figure 1 summarizes the learning curves, final test scores, and routing agreement.
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All authors Tianyu Chen, Yasi Zhang, Ruiyi Wang, Xinran Zhao, Taoran Li, Mingyuan Zhou
Multiagent Systems
Where Do Multi-Agent Systems Fail? Evidence-Grounded Diagnosis of Collective Mechanisms
Zhengye Han
When a multi-
agent system answers correctly, it is tempting to conclude that its
agents shared, checked, and used information as intended. Yet a system can break one of its collective mechanisms, the rules that govern how
agents route, admit, store, and act on shared information, and still return the right answer, while a wrong answer rarely reveals which mechanism failed. We ask what evidence from an execution is sufficient to conclude that a particular mechanism was violated. Our answer is a diagnostic contract, which separates what counts as a violation from which execution records can establish one, and concludes that a violation is supported, ruled out, or unknown; removing records can make this conclusion unknown but never reverse it. We test contracts for four mechanisms by replaying executions from the step where a mechanism acts, once unchanged, once with the mechanism broken, and once with it restored. Broken mechanisms often left the answer correct. An LLM diagnoser detected many more violations from internal records than from public outputs, yet with identical records a generic
prompt often claimed certainty the records did not support, which
prompts stating the contracts largely avoided. The contracts also applied, in narrow form, to mechanisms in independently developed systems, but a diagnostic behavior that was nearly perfect on our
benchmark degraded on an independently developed workflow. A correct outcome is therefore no substitute for records of how collective mechanisms operated, and agreement on one
benchmark does not show that a diagnoser transfers to another system.
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Robotics
HALO: Heterogeneous Allocation Via Localized Observations for the Vehicle Routing Problem
Andrew Meighan, Hyungsub Kim and Or Dantsker
Scalable robotic fleets have become increasingly popular for various applications such as package delivery, warehouse management, and military operations. Prior fleet control
algorithms solve centralized routing problems with up to $1{,}000$ tasks in controlled environments, yet they fail to consider realistic constraints such as limited observation and communication ranges typical of decentralized fleets. Thus, deploying existing fleet control
algorithms into real-world settings is currently infeasible. To tackle this, we propose Heterogeneous Allocation via Localized Observations (
HALO) to solve the Vehicle Routing Problem (VRP).
HALO is a hybrid method that splits the VRP into allocation and routing portions to provide onboard, real-time solutions to
robots in dynamic environments. During the allocation phase,
HALO utilizes a heterogeneous graph neural network framework with unique message passing layers to explicitly separate the learning of spatial distributions and task-to-
robot compatibility. Evaluation results on a partially observable, online variant of the VRP show
HALO significantly outperforms the heuristic baseline while maintaining similar solution quality to an all-knowing offline variant of
HALO. While
HALO is explicitly designed for partially observable environments, it imposes no strict upper bound on the observation space allowing us to test
HALO on the traditional static, single-depot VRP. Here,
HALO outperforms state-of-the-art architectures strictly optimized for the static variant of the VRP by up to $14.06\%$. Throughout all testing, this framework maintains the quickest execution times which emphasizes its potential for large-scale, real-time deployment.
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Computer Vision
Event-Driven Refresh and Recurrence Memory to Reduce Stale Grounding in Referring Video Object Segmentation
Abu Hanif Muhammad Syarubany, Jaehyun Jang, Siwoo Lim et al. (+2 more)
Referring
Video Object
Segmentation (RVOS) aims to produce a pixel-accurate mask sequence for an object specified by natural
language. Sa2VA combines a
multimodal large
language model with SAM2 for grounded
segmentation; however, its inference typically grounds the query from a small fixed set of initial keyframes and then relies on propagation. In long or dynamic
videos, this can cause stale grounding and persistent false positives when the object composition changes (e.g., distractors enter or the target disappears/re-appears). We propose Event-Driven Refresh + Recurrence
Memory (EDRRM), an enhancement that selectively re-invokes Sa2VA only at stable change points. EDRRM triggers refresh boundaries using an EMA-smoothed event score computed from tracking-derived cues (births/deaths and coarse composition/layout changes) with temporal constraints. A recurrence
memory further retrieves anchor frames via CLIP similarity to re-condition the model on re-appearance events. Experiments on Ref-DAVIS17, MeViS, and ReVOS show that EDRRM achieves a competitive accuracy-efficiency trade-off relative to fixed-window and FrameDiff-SSIM baselines, maintaining comparable or superior J&F scores at substantially lower average refresh-call budgets and reducing false-positive failures. End-to-end runtime analysis further confirms that the overhead introduced by tracking, CLIP-based recurrence matching, and the identifiability gate remains modest relative to the dominant Sa2VA inference cost, thereby validating the efficiency of the proposed pipeline.
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All authors Abu Hanif Muhammad Syarubany, Jaehyun Jang, Siwoo Lim, Seungyeon Ryu, Chang D. Yoo
Artificial Intelligence
Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization
Chen Lu, Ke Xue, Siyuan Xu et al. (+2 more)
Large
language models are increasingly participating in complex real-world tasks in the form of
algorithm-design
agents, designing and refining
algorithms. Many successful
algorithm-design
agents adopt pure in-context evolutionary frameworks, but they may quickly plateau in domains that require specialized knowledge. Parametric adaptation offers a way to internalize specialized knowledge, but conventional training requires abundant domain-specific corpora while high-quality
algorithms are scarce in complex
algorithm-design scenarios. In this paper, we propose sample-efficient parametric self-
evolution where
agents can explore and learn from self-generated
algorithms. First, we characterize in-context evolutionary stagnation and analytically propose the Improvement Chain proposition, showing how learning successive self-generated
algorithms can locally increase the likelihood of neighboring
algorithms. Motivated by this local-transfer perspective, we further propose Population-Curated Policy Optimization (PCPO) to utilize a global population and a hybrid policy update scheme for retaining and reusing high-quality, diverse self-generated
algorithms, shifting the policy towards stronger
algorithms. In the task of learning rate schedule design for global placement in electronic design automation, trained only on 4 chip cases, PCPO outperforms the state-of-the-art in-context evolutionary methods (e.g., OpenEvolve and ShinkaEvolve) on average across 16 chip cases. With an 8B-size base model, PCPO achieves competitive performance compared to frontier closed-source models such as GPT-5.5. PCPO also reduces inference-time
token cost by internalizing grounded domain knowledge and
prompt distillation. Moreover, PCPO achieves significant speedups on four GPU kernel designs, with an average of 8.27$\times$ speedup against the PyTorch Eager baseline.
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All authors Chen Lu, Ke Xue, Siyuan Xu, Mingxuan Yuan, Chao Qian
Computer Vision
Agentic Relative Camera Pose Estimation via Learned Ranking and Verification
Zhining Gu, Shangjie Du, Weimin Qiu et al. (+3 more)
A wide range of approaches have been developed for camera pose estimation, including correspondence-based methods, end-to-end pose regression, and recent 3D
geometric foundation models. Our key observation is that no single
estimator is optimal for diverse challenges, such as wide baselines, lack of texture, appearance changes, and occlusions. Further analysis reveals substantial performance variation across both
benchmarks and individual image pairs, with different
estimators exhibiting complementary strengths. We introduce PoseAgent, an agentic framework for relative camera pose estimation that dynamically orchestrates pose
estimators through learnable ranking and verification. Given an image pair, a profiling
agent first extracts appearance, semantic, and
geometric features relevant to pose estimation, e.g., scene type. A learned ranking
agent then predicts the relative competence of multiple pose
estimators given the image-pair profile. The top-ranked
estimator is executed, and its predicted pose is assessed by a learned verification
agent that estimates the corresponding pose error. When verification fails, PoseAgent adaptively invokes lower-ranked
estimators until a candidate is accepted or the execution budget is reached. For pose verification, our verification network predicts pose errors more accurately than prior models. For pose estimation, PoseAgent improves AUC@5 degree up to 4.2% over the strongest standalone
estimator on each of ARKitScenes, MegaDepth, ScanNet++, and RealEstate10K. On ARKitScenes, PoseAgent also outperforms VLM-based
agents, which include a VLM ranker with the same verifier and fallback policy. These results demonstrate the effectiveness of our learned ranking and verification.
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All authors Zhining Gu, Shangjie Du, Weimin Qiu, Carl Olsson, Ping Liu, Meng Tang
Computational Engineering · Cross-listed from math.OC
Reclassified to Conform
Chris Martin
A company's financial statements over many years must be assembled from many filings. Each filing may rename, combine, split or move its lines, and restates only the last two or three years to match. Filings provide no consistent, machine-readable mapping between successive statements, and neither a line's label nor its XBRL tag is fixed. We treat the reconstruction as an optimization problem. A tag that keeps its numbers from one filing to the next is taken to be the same line, and such runs are joined into strands before any edit is weighed. At each boundary between filings, the candidate edits that pass the check their kind requires are listed, each with the strand endpoints it would explain and a price for how much it asks the reader to believe beyond what the filings show. An integer program picks the cheapest set of the listed edits that accounts for every line exactly once, and the chosen edits trace each line through the filings. The result is a statement whose rows follow one line across every year, comparable within each filing's restated window and marked where a number was restated or derived, in any filing's layout, with each number cited to the filing that stated it and each declared total re-checked. On 200 companies drawn at random, 99.9% of the totals that can be checked add up. Among the 124 with a usable vendor comparison, the lines the method follows agree with the vendor's series on 96% of the
cells both fill, reach 10% more
cells than a tag alone and agree with the vendor on 90% of those. The method also yields a record of what each company changed in its reporting.
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Human-Computer Interaction
Where the Evidence Lives: Auditing AI Companions' Self-Descriptions
Seiya Ikeda and Shin-nosuke Ishikawa
Companion
agents describe themselves: they remember, they understand their users, the relationship has changed them. We argue that such accounts, and the experience ratings that seem to confirm them, are checkable by users only where the evidence is theirs: in the
agent's behavior, or in themselves. Where the evidence lives in the machinery, fluent self-description and moderately positive ratings do not establish that the mechanisms behind them ran. We demonstrate an audit procedure that sets an
agent's self-description against its users' judgements and its implementation records, reporting each claim as supported, contradicted, or unresolved, and apply it to Lita, a proactive companion we built and deployed for a month with nine colleagues. Participants endorsed stylistic claims, withheld endorsement from relational ones, and rated
memory at or above midpoint, while two of three
memory layers had never executed their accumulation step.
Memory-bearing
agents should report what their self-descriptions cannot establish.
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Networking
Can LLMs help find Ambiguities in Protocol Specifications?
Ziyue Dang, Sixu Tan, Atharva Nevasekar et al. (+3 more)
Internet protocol specifications written in RFCs are subject to ambiguities and multiple interpretations that can cause interoperability failure. While these have presumably cleared up after years of experience, such ambiguities can bedevil the adoption of newer protocols like 5G. The 5G specifications pair a formal message syntax (ASN.1) with message-handling procedures written in natural
language. This creates semantic underspecification: a syntactically valid message can reach a state whose procedures never say how to handle it, so standard-compliant implementations diverge. We frame this as a gap or a fork in a partially specified communicating state machine, and present SpecLens, which puts that view in front of a
language model as a scaffold. Stronger models do not remove the need for it: they broaden the search without disciplining it, and fewer than half their findings survive inspection. Across 36 procedures from six 3GPP and O-RAN protocols, experts accept 185 of 197 SpecLens findings, and 60 drive observable divergence between the OpenAirInterface and srsRAN implementations under differential test. While we use 5G as a canonical example of a newer protocol, we also show ambiguity results for the more mature DNS protocol.
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All authors Ziyue Dang, Sixu Tan, Atharva Nevasekar, Zhaowei Tan, George Varghese, Songwu Lu
Numerical Analysis
Worst-Case Completion of Tensors with Approximately Few ANOVA Terms
Simon Foucart and Jingchun Shao
In this article, the problem of completing a tensor from some incomplete knowledge of its entries is treated by adopting a worst-case perspective, given the realistic assumption that the tensor's low-order ANOVA terms are dominant. We survey and leverage some recent all-purpose results from the field of Optimal Recovery to provide solutions on a theoretical level. But the accompanying constructions of optimal completion procedures, which often feature semidefinite programs, are not directly applicable in the tensor case due to the huge dimensions involved. To resolve the issue, we put forward a storage-friendly way to produce low-order ANOVA projections based on the fast
Fourier transform (FFT), while exploiting the specificities of the completion problem to efficiently compute regularizers and extremal
eigenvalues. Numerical experiments on synthetic tensors and real-world
datasets demonstrate the accuracy and scalability of our FFT-based method.
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Computer Vision
Hear the World in Stereo: Learning Dynamic Spatial Correspondence for Immersive Joint Video-Audio Generation
Hanmo Chen, Chengcheng Liu, Tianxiao Chen et al. (+7 more)
Recent joint
video-audio generation models have achieved strong semantic correspondence and temporal synchronization. However, applications such as AR/VR and interactive gaming further require stereo audio to provide an immersive sense, which remains largely overlooked. Effective stereo audio requires the perceived sound location to evolve consistently with the motion of its corresponding visual source. We refer to this property as Dynamic Spatial Correspondence and propose StereoBind, a framework that binds visual source motion to stereo sound generation. StereoBind uses motion tracks to coordinate visual motion and stereo audio through three complementary mechanisms. Visual Motion Binding establishes source-aware audiovisual correspondence, the Spatial Track Encoder captures absolute source positions, and Residual Track RoPE models relative motion. For supervision and evaluation, we construct StereoWorld-29K, a large-scale stereo audio-
video dataset with paired motion tracks, and StereoWorldBench for measuring audiovisual spatial consistency. Experiments show that StereoBind substantially improves spatial
alignment in stereo audio generation over existing models while preserving overall audiovisual quality.
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All authors Hanmo Chen, Chengcheng Liu, Tianxiao Chen, Zheyu Zhang, Siming Zheng, Jinwei Chen, Xu Yang, Cheng Deng, Bo Li, Peng-Tao Jiang
Computer Vision
Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection
Junyang Xia, Luocheng Zhang, Wenwen Pan et al. (+4 more)
Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliary reference samples. The task remains difficult because reference samples contain inconsistent target cues, while generic visual representations are not inherently aligned with the target specified by the references. To handle these problems, we present a consensus-aware multi-source fusion framework. Reference-Conditioned Dual-Backbone Fusion (RCDF) couples trainable PVTv2 query features with frozen DINOv3 representations and uses reference-conditioned correlation to select foundation-model evidence before multi-scale fusion. The framework also aggregates multiple references through cross-reference consensus aggregation and injects reference information at semantic depths matched to the query features. Extensive experiments demonstrate the effectiveness of the proposed method. The results further show that reference consensus, target-conditioned foundation features, and hierarchical decoding provide complementary improvements under the evaluation protocol. The source code will be made publicly available upon acceptance.
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All authors Junyang Xia, Luocheng Zhang, Wenwen Pan, Chifeng Zhu, Yang Yang, Xinchun Liu, Jiajun Ding
Computer Vision
Matisse: Evidence-Space Reasoning for Active 3D Reconstruction
Xihang Yu, Kaichen Zhou, Lorenzo Shaikewitz et al. (+4 more)
How can a 3D reconstruction system acquire and retain useful information to understand the geometry of a scene from partial views under a limited computation budget? Existing active view acquisition methods typically estimate uncertainty over observed or instantiated geometry, limiting their ability to reason about unseen structure, while long-horizon reconstruction methods often retain redundant observations. We introduce Matisse, a training-free framework that unifies active reconstruction and keyframe selection by leveraging evidence provided by a pretrained generative 3D model. Matisse estimates Evidential Uncertainty from cross-
attention evidence associated with 3D latent
tokens and derives an Evidential Information Gain to guide both view acquisition and keyframe selection based on the expected reduction in posterior
entropy. Matisse supports multi-object scenes through occlusion-aware, object-balanced aggregation and propagates uncertainty through intermediate latents to avoid full reconstruction during planning. Matisse reduces Chamfer distance by 12.7%, 3.8%, and 9.2% on GSO30, YCB-V, and Replica, respectively, relative to the best baseline on each
dataset, and achieves a $1.50\times$ end-to-end speedup over the best active reconstruction baseline on GSO30 with the same reconstruction backend. In the GSO30 keyframe selection experiment for long-horizon reconstruction, Matisse achieves comparable Chamfer distance using 14% of the input views compared with Stream3D.
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All authors Xihang Yu, Kaichen Zhou, Lorenzo Shaikewitz, Clément Jambon, Xiao Zhan, Rajat Talak, Luca Carlone
Machine Learning
Molecular Property Prediction under Structural Shift with Tabular Foundation Models
Jinmo Lee, Dooho Lee, Minho Jeong et al. (+1 more)
Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials design. Tabular foundation models (TFMs) offer a promising approach through in-context learning, but their performance under structural shifts and the value of molecular comparisons in this setting remain underexplored. We study structural generalization in molecular property prediction and introduce MolPAIR (Molecular Pair-Augmented In-context Refinement), a framework that combines molecule-level and molecular-pair contexts without task-specific parameter updates. A global tabular foundation model (TFM) first predicts a query's property from labeled molecular examples. A second frozen TFM predicts differences in prediction errors between the query and labeled reference molecules, using these comparisons to refine the initial prediction. Across 58 MoleculeACE and Polaris tasks, CheMeleon representations combined with TabPFN-3 already outperform each evaluated baseline on a majority of tasks. MOLPAIR further improves this predictor on 46 of 58 tasks, with gains across four molecular representations and three TFM backbones. These results show that explicit molecular comparisons can strengthen tabular in-context learning for structural generalization while keeping the molecular encoder and pretrained model weights fixed. The code and
datasets are available at https://github.com/nums-ai/MolPAIR.
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All authors Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo