Artificial Intelligence · Cross-listed from cs.SE
Studying, Identifying, and Fixing Hidden Technical Debt in AI-Intensive Cyber-Physical Systems
Beena
Artificial Intelligence (AI) components are increasingly pervasive in several software systems, including Cyber-Physical Systems (CPSs). AI-CPS are used in several domains, including autonomous vehicles, industry, home automation, robotics, and healthcare. Being composed of hardware, AI components, and conventional modules, AI-CPS can exhibit technical debt (TD) that is peculiar and potentially more challenging than that of conventional systems. This thesis aims to characterize AI-CPS TD and propose approaches for its identification and repair. In a first phase, we characterize AI-CPS TD by analyzing AI ecosystems and AI-CPS repositories, as well as interviewing developers. Based on the acquired knowledge, we define approaches to identify and mitigate such TD. Finally, we plan to develop and validate an automated tool that supports agentic AI solutions to monitor, govern, and repay AI-CPS TD.
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Artificial Intelligence · Cross-listed from cs.SE
Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds
Yuxuan Liu, Zhaochen Su, Yuhao Zhang et al. (+9 more)
Self-evolving skill systems promise to improve
agents by turning execution feedback into persistent skill updates without changing the underlying model. Yet it remains unclear when further
evolution helps, how successful and failed trajectories shape revision, and whether extra test-time computation can recover the same gains. To address these questions, we present a controlled evaluation framework across five
benchmarks and three models. Our primary study contains 42 feedback runs across 14 supported model-
benchmark settings. Within each setting, we hold the executor and optimizer configuration, revision procedure, validation rule, and round budget fixed, while varying only the feedback shown to the optimizer: successes and failures (Normal), failures only, or successes only.
Evolution is sparse: only 55 of 388 candidates establish byte-distinct validation bests. Validation-based selection chooses an evolved skill in 11 of 14 settings, nine of which improve released-test performance. All 11 selections come from feedback conditions that include failed trajectories, although the relative ranking of Normal and Fail-only varies across settings. Validation and downstream evaluations on test, robustness, and transfer sometimes favor different feedback views. A broader SearchQA analysis covering eight models shows similarly sparse, feedback-dependent dynamics. In the GPT-5.5 test-time-scaling controls, oracle Parallel Sampling comes within 0.43 points of the evolved SearchQA skill but remains 30.96 points behind on SpreadsheetBench; Sequential Refinement recovers neither gain. Overall, persistent skill self-
evolution is better understood as sparse, validation-filtered search with model- and
benchmark-dependent returns, rather than steady improvement from additional rounds. The implementation is available at https://github.com/HKUST-KnowComp/rethinkskill.
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All authors Yuxuan Liu, Zhaochen Su, Yuhao Zhang, Jiahe Guo, Zhongwei Xie, Huihao Jing, Lingyun Xie, Qing Zong, Yauwai Yim, Zhixiong Zhang, Haoran Li, Yangqiu Song
Artificial Intelligence · Cross-listed from cs.CV
ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
Renjie Liang, Zijian Xu, Jinqian Pan et al. (+6 more)
A 3D CT scan entering a
vision-
language model produces a long sequence of visual
tokens, often thousands to tens of thousands per volume, and this sequence must be compressed before a
language model can consume it.
Token compression is well studied in general
vision, but little of it targets 3D CT specifically. A common baseline is grid average, which pools regular grid
cells and can blend distinct anatomy, lesion, and air into one
token. We present ORCA (ORgan-Centroid Aggregation), a
token compressor for 3D CT. It merges adjacent
tokens with organ guidance and adds a sinusoidal encoding of each region's centroid to preserve spatial layout. This preserves the anatomical information a downstream model needs. ORCA is training-free and plug-and-play, producing an adjustable
token set without any model change or text query. We evaluate it across two
datasets (CT-RATE and Merlin) and five encoders. The evaluation spans two task types: attribute prediction over five families (size, density, location, texture, and disease) and text generation (visual question answering and report generation). At matched
token budgets, ORCA improves consistently over existing compression methods. It shrinks the visual context $64\times$ and its KV-cache $50\times$, and is $31\times$ faster to process each volume. Code released at https://github.com/renjie-liang/ORCA-3DCT.
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All authors Renjie Liang, Zijian Xu, Jinqian Pan, Chengkun Sun, Zhengkang Fan, Shawn Li, You Qin, Mei Liu, Jie Xu
Artificial Intelligence
Eddie Yang
AI
agents increasingly perform open-ended tasks in settings where their conclusions can guide consequential decisions. We provide evidence that AI
agents draw different conclusions from identical numerical data when the substantive framing changes. We demonstrate this behavior in high-stakes domains in medicine, election forensics, and geopolitical
forecasting by holding the evidence fixed while changing the scenario in which the evidence appears. Across twelve
agent-domain comparisons,
agents' conclusions are strongly influenced by their prior beliefs. They are more likely to reach an affirmative conclusion when it is framed around a proposition they already regard as likely, while the reverse holds when the framing conflicts with their prior. The framing also changes how some
agents work: they search more extensively, choose different analytical specifications, and evaluate the same evidence differently. These results identify a particular risk of delegating decision-making to AI
agents, as their decisions may depend on prior beliefs that are neither specified in the task nor visible in the decision record.
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Artificial Intelligence
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
Chengbo Liu, Lifang Zhou, Ruijie Yan et al. (+8 more)
Compact web
agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL). Successful trajectories are expensive to collect and often contain inefficient detours. After supervised fine-tuning (SFT), full trajectory corpora are dominated by routine states; moreover, when group-relative RL is applied to web actions, inadequately designed action-level rewards can yield weak or misleading relative updates, while groups rejected as unsuitable for such updates receive no fallback learning signal. We present RMSWeb, a three-part recipe for Qwen3-VL-Instruct at 8B and 32B. Reflection-conditioned retries increase collection yield and shorten successful trajectories; failure-mode mining concentrates offline RL on critical states exposed by the SFT policy; and Salvage-DS combines an action-semantic polarized reward, contrast-and-competence-gated dynamic sampling, and an action-only anchor for rejected groups. Policies trained with reflection-collected data use up to 19.7% fewer action steps on solved tasks. On WebVoyager, Online-Mind2Web, and WebTailBench, RMSWeb improves over SFT by 2.4-7.0 points at 8B and 1.2-7.7 points at 32B. Our 8B model also achieves the strongest reported Online-Mind2Web result among similarly sized open-weight models in our comparison and a leading reported accuracy-cost trade-off on WebVoyager and WebTailBench, with the caveat that external evaluation protocols differ.
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All authors Chengbo Liu, Lifang Zhou, Ruijie Yan, Pei Tan, Ao Sun, Haojun Huang, Guichun Hua, Sining Wei, Yining Chen, Yingying He, Yutao Xie
Artificial Intelligence
Learning to Coordinate Symbolic Tools: LLM Agents for Verified Sum-of-Squares Certificates
Bohan Chen, Shivam N. Patel, Richard Hoffmann et al. (+2 more)
Tool calling allows large
language models (LLMs) to invoke external computation during problem solving, a useful capability in various fields including AI for mathematics. We study this setting through weighted sum-of-squares (SOS) decomposition, a machine-checkable route to proving
polynomial nonnegativity and hence
polynomial inequalities. A candidate decomposition can be checked exactly, but finding one requires choosing among non-unique regroupings and coordinating multiple symbolic transformations. We develop an
agent that combines algebraic task training, symbolic tools, and verifier-grounded optimization for this task. Rather than training only on the composite SOS task, we construct 1.35 million synthetic examples covering eight supporting
polynomial tasks together with weighted-SOS decomposition. We first apply supervised fine-tuning (SFT) to direct
algebra problems and simulated symbolic traces, and then use Group Relative Policy Optimization (GRPO) with task-specific symbolic rewards. The SFT corpus contains no native tool-calling messages; at evaluation, the
agent uses native SymPy calls for expansion, collection, reordering, and factorization. Every final SOS answer is checked by exact expansion and coefficient comparison. On held-out, same-generator synthetic problems, the full SFT+GRPO+tools system is the strongest of four evaluated configurations, reaching 78.96% verified success on weighted SOS, compared with 44.73% for the base model with the same tools, and 91.75% macro accuracy across nine
polynomial tasks. Within this controlled setting, our work provides a case study of combining domain-specific skill training, executable tools, and verifier feedback, and may inform the design of tool-calling
agents in other domains with exactly checkable outputs.
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All authors Bohan Chen, Shivam N. Patel, Richard Hoffmann, Sam Looi, Tony Yue Yu
Artificial Intelligence
Trust and Its Betrayal under Three Representational Strategies
Mihnea C. Moldoveanu and Joel A.C. Baum
Trust is a propositional attitude of a distinctive kind: to trust is to rely on another under conditions where reliance could be disappointed, and the disappointment of trust—betrayal—differs qualitatively from the disappointment of a prediction. We treat trust as a subjunctive epistemic state: $A$ trusts $B$'s competence when $A$ believes that \emph{were $P$ true, $B$ would know it}, and $B$'s integrity when $A$ believes that \emph{were $B$ to know $P$, he would disclose it to $A$}. We develop three representations of this state—as lexicographic assumption as ordinal closeness in a Lewis–Stalnaker sphere system , and as strong belief in a conditional probability system and for each we ask whether the Brandenburger–Keisler impossibility on common belief survives when the assumption of rationality is replaced by an assumption of trustworthiness. The three representations agree that every finite depth of common trust is realizable while the completed common-trust fixed point is the locus of difficulty, but they differ sharply in how the difficulty manifests, and—our organizing finding—in how each survives a concrete betrayal. W show that the same betrayal refutes an
agent's level ordering under the lexicographic representation, contaminates her closeness ordering in proportion to the betrayer's deliberateness under the ordinal representation, and merely shifts her operative conditioning hypothesis while leaving her belief structure coherent under the strong-belief representation.
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Artificial Intelligence · Cross-listed from cs.SE
OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning
Amr M. Zaki, Farhoud Jafari Kaleibar, Honggeun Ji et al. (+2 more)
We introduce Orthogonal-Edge (OrEdge), a lightweight framework for real-time anomaly detection in multi-modal distributed software systems. Unlike existing approaches that rely on computationally expensive
attention- and graph-based architectures, OrEdge leverages orthogonal-domain temporal representations to achieve accurate anomaly detection with substantially lower computational complexity and model size. It jointly analyzes heterogeneous monitoring data, including logs, metrics, and traces, to identify abnormal software behavior, capture temporal dependencies, and reduce redundancy across observability signals. At its core, OrEdge incorporates OrEdgeCore, a lightweight orthogonal-domain reconstruction module that captures recurring temporal patterns while suppressing transient variations. Evaluated on three real-world microservice
datasets (MSDS, SN, and TT), OrEdge achieves competitive detection performance while reducing the reconstruction model size to at most 9.6K parameters, compared with 20K–143K parameters in existing methods. This compact design enables efficient deployment on resource-constrained edge devices: on Raspberry Pi platforms, OrEdge achieves sub-second inference and reduces inference latency by over an order of magnitude compared with existing approaches. Extensive ablation studies, sensitivity analyses, orthogonal basis evaluations, and qualitative case studies further validate the effectiveness of each design component. Overall, OrEdge demonstrates that orthogonal-domain temporal modeling provides an effective alternative to computationally intensive
attention- and graph-based architectures, achieving a favorable balance between detection accuracy and computational efficiency for real-time multi-modal anomaly detection in edge environments. The code is available at https://github.com/theamrzaki/MicroService_Twin_Original.
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All authors Amr M. Zaki, Farhoud Jafari Kaleibar, Honggeun Ji, Komal Sarda, Marin Litoiu
Artificial Intelligence
CrystalMem: Elastic Memory for Self-Evolving LLM Agents via Knowledge Crystallization
Beining Wu and Jun Huang
Memory for self-evolving large
language model (LLM)
agents is often provisioned as if its byte budget only grows. Cloud platforms, however, adjust quotas with load and cost, and we show that capability does not follow the budget back up: after a squeeze-and-recover cycle, the
agent settles below its pre-squeeze level, a gap we call
memory hysteresis. The cause is structural. Deletion and one-way compression discard the material needed for later rebuilding, and we prove that any policy that only keeps or drops entries carries a residual-deficit floor. We propose CrystalMem (Crystallized
Memory), an elastic
memory sidecar that demotes entries across four fidelity states under a crystallization-energy schedule, orders demotions by advantage-weighted influence with dependency coupling, and recovers capability through verified recrystallization under explicit compute and byte caps. Across seven environments, seventeen methods, and six backbones, with multi-tenant serving and a physical edge-cloud deployment, CrystalMem achieves the highest restored capability in every setting and closes the loop left open by every baseline. From a 50% byte budget, CrystalMem matches the strongest budgeted baseline at full provision on every environment; at equal budgets, it leads by +4.6 pp on average.
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Artificial Intelligence
WM-Cov: Test Adequacy for Interactive World-Model-Style Autonomous Driving Simulation
Jianxun Cui, Ping Wu, Stanisa Peric et al. (+2 more)
World models and generative simulators are emerging as interactive testing infrastructure for autonomous driving because they can react to the ego planner and produce counterfactual, rare, and safety-critical rollouts. This changes a test scenario from a fixed replayed trajectory into an interactive scenario family whose realized
evolution depends on the planner under test. The unresolved question is therefore not only whether dangerous rollouts can be generated, but what valid closed-loop evidence is enough to support a specified testing intent and stopping decision. This paper formulates interactive world-model-style testing adequacy and introduces WM-Cov, a provider-agnostic evaluation layer that converts raw provider outputs into requested, realized, and valid evidence. WM-Cov reports adequacy through coverage growth, valid-failure discovery, failure-mode diversity, realism, artifact suppression, duplicate accounting, and valid-evidence precision. Studies on executed TeraSim/SUMO events, WM-like mixed trace pools, and a real DriveArena TrafficManager–WorldDreamer
matrix show that dangerous-looking events can include valid ADS failures, duplicates, partial realizations, and artifacts. The DriveArena
matrix evaluates two planners, two horizons, six
prompt conditions, and 360 ego-route requests; 304 attempts become fully realized evidence and 56 remain partial. A disjoint 80-request route-slice check yields 74 fully realized and 6 partial attempts. The results support evaluating world-model-style testing by convergence of valid interactive evidence under budget, rather than by raw generated failures or
prompt coverage alone.
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All authors Jianxun Cui, Ping Wu, Stanisa Peric, Marko Milojkovic, Vladan Devedzic
Artificial Intelligence
RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals
Lihao Wang, Linlu Gao, Jiacan Yu et al. (+5 more)
Recognizing Human-Object Interactions (HOI) is essential for intelligent systems, underpinning applications in virtual and augmented reality,
embodied AI, and assistive robotics. However,
vision-based HOI methods face challenges in
privacy concerns and poor light conditions. In this work, we introduce RF-HOI, the first framework that only uses
radio frequency (RF) signals for HOI recognition. A key challenge of RF-HOI is that single-modality RF sensing is insufficient to recognize both actions and the objects being interacted with. RF-HOI addresses this through a novel modality fusion that combines mmWave radar and RFID, enabling simultaneous action recognition and target identification. Another challenge is limited training data across diverse setups, which impairs the generalizability of the recognition model. To overcome this, we develop a simulator that synthesizes
multimodal RF data for diverse HOIs at scale, allowing us to fine-tune with only a small amount of real-world data. Experiment results show that RF-HOI outperforms all baselines, approaching
vision model performance, and that our diverse synthetic training data can significantly boost our system's performance on real-world scenarios. These results highlight the potential of
multimodal RF sensing for robust and
privacy-preserving HOI recognition as well as the effectiveness of our RF data synthesis.
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All authors Lihao Wang, Linlu Gao, Jiacan Yu, Yanyu Lin, Yifan Yin, Jianxin Wang, Tianmin Shu, Renjie Zhao
Artificial Intelligence · Cross-listed from cs.CL
Sixteen models, fewer than two voices: measuring ensemble dispersion where no answer is uniquely correct
Mario Vega-Barbas, Lidia Mora-Valenciano, Iván Pau et al. (+2 more)
Sixteen
language models drawn from ten families produced, on average, the semantic diversity of 1.69 distinct formulations of a psychotherapeutic case, against a single-model baseline of 1.43 from one model's own runs. Ensembles place more than one reading before a decision-maker on the premise that several models supply several perspectives. Dispersion over their outputs is measured both as diversity and as uncertainty, and both traditions validate it against a correctness criterion that this task does not admit. Measuring diversity is a solved problem: the Vendi Score, the exponential of the von Neumann
entropy of a similarity
matrix, is an effective number of distinct elements. What a single aggregate does not say is where the diversity comes from. We define a per-model dissent contribution, the complement of a model's mean similarity to the other members of its ensemble: a magnitude from the same
matrix, not a decomposition of the spectral index, whose maximum identifies the most divergent voice. Crossing model and case, we test as a preregistered hypothesis whether model identity accounts for a non-zero share of the variance in dissent, and characterise the structure that test detects. The panel formulated fifteen stratified vignettes, yielding 7,082 formulations for analysis. Model identity was a detectable structuring factor of the dissent that remained, but the usual categories recovered it only partly: scale differences pointed in opposite directions across pairs, family grouped models on only five two-member lines, and the most divergent voice changed with panel composition, so that the surfaced outlier describes the ensemble rather than the model. Dissent did not track the interpretive openness for which the case bank was stratified; it was organised by clinical content instead, leaving the dispersion an ensemble produces a property to measure rather than assume.
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All authors Mario Vega-Barbas, Lidia Mora-Valenciano, Iván Pau, Fernando Seoane, Farhad Abtahi
Artificial Intelligence · Cross-listed from cs.RO
Hybrid Attention Estimation Pipeline for Adaptive HRI Using an Expressive Robotic Head
Pablo Moraes, Monica Rodriguez, Christopher Peters et al. (+4 more)
This paper presents an applied case study on hybrid visual
attention estimation for human-
robot interaction using an expressive robotic head based on the InMoov ecosystem. The proposed pipeline combines a fast
geometric perception layer with an independent semantic perception layer based on a
vision-
language model. The
geometric layer provides high-frequency face and head-pose information for temporal regulation, while the semantic layer receives only raw
egocentric camera frames and produces contextual
attention labels related to
attention toward the
robot, phone use, or
attention elsewhere. These signals are integrated through a finite state machine that regulates adaptive interaction behavior, including activation, waiting, interaction resumption, and return to rest. The system was evaluated with 10 participants across 40 trials covering baseline and adaptive interaction conditions. Results show reliable interaction start across all trials, consistent pause behavior in the adaptive distraction condition, and non-redundant semantic information between the
geometric and semantic outputs.
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All authors Pablo Moraes, Monica Rodriguez, Christopher Peters, Hiago Sodre, Tobias Doernbach, Bruna Guterres, Ricardo Grando
Artificial Intelligence
Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
David Aguado, Daniel Fuertes, Carlos R. del-Blanco et al. (+1 more)
Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact
algorithms for solving routing problems such as the Traveling Salesman Problem (TSP). However, the generalization capabilities of these Reinforcement Learning-based models are severely hindered when scaling to high-dimensional instances. This issue has been mitigated in other domains, like computer
vision and natural
language processing, by adopting a self-supervised pre-training strategy. Nevertheless, its application to routing graphs, which lack complex topological attributes beyond 2D spatial coordinates, remains a challenge. In this paper, we propose a
geometric self-supervised pre-training framework specifically designed to capture spatial invariance and global relative distance distributions. By applying isometric transformations, such as rotations and axial reflections, the model learns robust structural representations prior to the policy optimization phase. Empirical results demonstrate that this strategy consistently outperforms models trained from scratch (baselines), achieving a 7.23% improvement in tour length for massive zero-shot extrapolation scenarios (TSP1,000). Furthermore, the proposed model exhibits remarkable computational efficiency, delivering speedups of up to two orders of magnitude over the exact solver Concorde at massive scales. The source code and pre-trained models are publicly available at https://github.com/davidaguadocosano/TSP-GeoPretrain.git.
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All authors David Aguado, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar
Artificial Intelligence · Cross-listed from cs.CV
Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk et al. (+2 more)
Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial computational power and
memory. To provide an interpretable-guided and efficient solution to this issue, we first propose a spectral analysis and new visualization technique for individual
attention heads based on the
Laplacian eigenvectors of their
attention maps. Building upon recent observations regarding the block structure of
Vision Transformers, we perform semantic clustering of
attention heads and identify functional redundancies. Leveraging these insights, we introduce SAPER (Soft
Attention PrunER), an end-to-end differentiable
pruning framework based on the LapSum Soft Top-K approach. Extensive experiments on ImageNet-1K demonstrate that SAPER achieves a highly favorable accuracy-efficiency trade-off, outperforming the competitive RAPTOR baseline in FLOPs reduction while preserving strong classification performance.
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All authors Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk, Jacek Tabor, Przemysław Biecek
Artificial Intelligence · Cross-listed from cs.CL
Cross-Task Dissociation in Frontier Vision-Language Model Theory of Mind
Kejia Zhang, Youran Sun, Chugang Yi et al. (+1 more)
Do frontier
vision-
language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that profile fragment from one paradigm to the next? We evaluate a shared panel of nine frontier VLMs on two psychology-derived
benchmarks: the Keysar Director Task (visual perspective-taking under
egocentric interference) and the Frith-Happé animated triangles scored with the Castelli rubric (intention attribution from pure motion). On the Director Task, without chain-of-thought, the panel makes the
egocentric error on 78% of trials like children rather than adults; variation is substantial across models, and
reasoning rescues several models. On the triangles, the panel under-attributes intention: its ToM profile sits more than three times closer to the high-functioning-autistic-adult (HF-ASD) mean than to the typical-development-adult (TD) mean, while Goal-Directed and Random stay near TD. No model is nearest TD on both tasks; the model that looks adult-like on the Director Task falls on the HF-ASD side on the triangles, and the most TD-like model on the triangles is child-like on the Director Task. We report group-level descriptions, not diagnostic labels for any model.
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All authors Kejia Zhang, Youran Sun, Chugang Yi, Haizhao Yang
Artificial Intelligence
More Debate, Same Evidence: Structural Limits of Homogeneous Multi-Agent Groundedness
Yuelyu Ji
Large
language model (LLM) judges are increasingly organized as multi-
agent panels under the assumption that exchanging critiques improves judgment quality. We test this assumption for groundedness verification, where a judge must determine whether a claim is supported by the supplied evidence. We evaluate a homogeneous three-
agent panel on six public fact-verification and
hallucination-detection
benchmarks. Relative to a fixed single-
agent reference, the panel's system-level accuracy difference ranges from $+8.5$ to $-4.4$ percentage points: two
datasets show reliable gains, one shows a reliable loss, and three are statistically inconclusive. Because the reference and panel use different model variants, these differences characterize the complete systems rather than isolate a causal debate effect.
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Artificial Intelligence · Cross-listed from cs.IR
Hierarchical BM25: Lexical Search at Billion-Document Scale
Umesh Deshpande and Swaminathan Sundararaman
A flat BM25 index over one billion documents occupies about 400 GB. Holding it in
memory requires DRAM proportional to corpus size. Serving it from disk takes 4-12 seconds per query. Exact top-k lexical
retrieval at this scale is therefore impractical within an interactive latency budget. Hierarchical BM25 gives up exact ranking in exchange for fixed bounds on
memory and latency. A resident coarse index selects which of ~1K topical, size-balanced document groups a query visits, using two signals: the total frequency of each query term within a group, and, for informative terms spread too thinly across groups for frequency totals to reflect, whether several of them appear together in one document. Selected groups are then searched exhaustively and scored against ~100 KB of global statistics. Every returned score therefore equals the flat index's score, and the approximation is confined to selection alone. The resident footprint is ~4.4 GB, independent of corpus size. Sixteen-term queries over one billion documents return in ~300 ms (4.7x to 5.6x the throughput of a flat multi-threaded index), and a warmed cache sustains ~32 queries per second versus under 3 for flat indexing. At a 500K-document configuration, visiting 5-10% of clusters recovers 0.83-0.92 of the exhaustive result score. Billion-scale recall and a direct comparison against document-reordered BlockMax-WAND remain open.
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Artificial Intelligence
Personalizing Large Language Model Agents with Small Policy Models
Dian Jin, Zhi Zhang, Huichao Li et al. (+3 more)
Large
language model (LLM)
agents can retrieve
memory, call tools, ask clarifying questions, and vary response style, yet adapting these execution decisions to an individual user remains difficult. Fine-tuning a separate LLM is costly or impossible for proprietary systems, while
prompts and
memory primarily expose user information to the
agent rather than adapt its execution decisions from feedback. We formulate personalization of a frozen
agent as online learning of a per-user execution policy from scalar feedback observed only for the executed action. We propose FABLE (Factorized Adaptive Bandit Layer for Execution), a lightweight policy layer outside a potentially black-box host
agent. FABLE factorizes
memory, information-acquisition, and response decisions so feedback updates related choices; filters actions through an externally specified feasible set before exploration; and learns user-specific residual preferences relative to a fixed default-and-cost score via
Bayesian contextual Thompson sampling. Under a linear residual-reward model, a calibrated variant inherits an expected-regret bound against the best feasible action. We also characterize preferences unidentifiable under persistent feasibility constraints and provide anytime-valid false-promotion control. Across personalized-
reasoning, controlled-feedback, and executable tool-use evaluations, FABLE improves several preference-sensitive behaviors relative to rule-only control while remaining competitive on end-to-end task performance.
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All authors Dian Jin, Zhi Zhang, Huichao Li, Yihe Pan, Rundong Huang, Doudou Zhou
Artificial Intelligence
TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding
Sparsh Rastogi, Tanmay Kumar, Baiyu Chen et al. (+3 more)
Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to activity labels. Recent LM-based approaches generate natural-
language explanations for sensor data, but their
reasoning is weakly grounded in the underlying signal, leading to fluent yet unverifiable explanations. We introduce TRACE-TS (Traceable
Reasoning with Attribution-Grounded Evidence), a framework for structured and signal-grounded
reasoning over wearable time series. TRACE-TS uses attribution from an expert classifier to identify salient spatio-temporal sensor regions, uses them to construct DAG
reasoning traces with explicit evidence provenance, and trains a compact
language model to generate these traces through gated cross-
attention over sensor
memory tokens. At inference, the adapted model jointly outputs the activity prediction and its
reasoning trace, without requiring attribution computation or teacher guidance. We introduce Semantic Node Match(SNM), an LLM-as-judge metric that diagnoses
reasoning fidelity at the observation, inference, and synthesis levels, localizing hallucinated observations and broken evidence chains missed by standard NLG metrics. Across seven wearable
benchmarks, TRACE-TS achieves the best average accuracy and F1 among all evaluated methods (84.43%/81.24%), and outperforms the best LLM-based baseline by 17.96% in F1. Our code is available at https://github.com/SparshRastogi/TRACE-TS.
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All authors Sparsh Rastogi, Tanmay Kumar, Baiyu Chen, Jatin Bedi, Zechen Li, Flora D. Salim
Artificial Intelligence · Cross-listed from cs.CE
Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval
Chenyu Wu and You Lin
Retrieval over financial filings is difficult because queries are short and acronym-heavy while the answer-bearing evidence sits inside long, table-dense documents. We study sparse-dense hybrid
retrieval on FinDER, a
benchmark of expert-annotated questions over corporate 10-K filings. Our first finding is methodological: if the
retrieval unit is larger than the dense encoder's input window, the dense model never sees a large share of the labeled evidence, confounding comparison against a full-text sparse baseline. We measure this directly and reduce the imbalance with windows chosen for the encoder budget. On this corpus, fusing BM25 and a compact dense encoder improves reference-level Hit@10 by roughly 28 percent over either component, and training-free, untuned reciprocal rank fusion exceeds the equal-weight blend in an exploratory comparison. We then ask whether choosing the fusion weight per query helps: an oracle over the interpolation-weight grid shows headroom of 21.8 percent, yet none of the three lightweight adaptive routers (a score-confidence heuristic, a random forest over query features, and a ridge regressor over query
embeddings) establishes a statistically reliable improvement over the fixed blend under company-grouped cross-validation with cluster-robust inference. Simple fusion is a strong baseline here, and we discuss why per-query weighting does not capture the available headroom.
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Artificial Intelligence · Cross-listed from cs.SE
Cross-Benchmark Generalization in Long-Horizon Agents
Sushant Mehta, Logan Ritchie, Liudas Panavas et al. (+1 more)
For reinforcement learning (RL) in self-contained environments, a policy can get rewards by exploiting environment-specific regularities (tool schemas, grader parsing, task templates) rather than by acquiring transferable skill, and an in-distribution holdout shares those regularities. We argue that the discriminating question is behavioral, namely how a trained
agent acts, and that cross-
benchmark transfer is the right place to look for it. We post-train an open-weight mixture-of-experts model (Qwen3.5-122B-A10B) on 363 long-horizon Model Context Protocol (MCP) tasks across 27 categories, using a two-stage SFT-then-RL pipeline. Toolathlon performance informed the initial base-family and SFT-teacher choices, but no external-
benchmark task or grader entered training and no external score informed the reward, training hyperparameters, trained-checkpoint selection, or stopping. At greedy pass@1, the trained model improves over the base on five reported external evaluations: Toolathlon (+9.6 pp), $\tau^2$-Bench (+5.3 pp), BFCL-V4 (+3.5 pp), SWE-Bench Pro (+5.8 pp), and Terminal-Bench 2 (+2.8 pp). Both software-engineering
benchmarks improve despite the training collection containing no software-engineering tasks. An exploratory paired-trajectory analysis identifies four recurring behavioral differences (more careful local-goal formation, building goal-relevant working state, keeping parent goals stable through local repairs, and verifying completion) that appear in analogous forms across office workflows and code. These results provide descriptive evidence that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain.
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All authors Sushant Mehta, Logan Ritchie, Liudas Panavas, Edwin Chen
Artificial Intelligence · Cross-listed from cs.LG
Inference-Time Policy Alignment for Fair Reinforcement Learning
Umer Siddique, Peilang Li, Conor Wallace et al. (+1 more)
Deep reinforcement learning (RL)
agents achieve strong performance by optimizing scalar reward functions. However, once deployed, the policies of these RL
agents are often rigid and costly to adapt to new performance criteria. For instance, an
agent trained to maximize expected cumulative reward may not accommodate previously unknown stakeholder preferences. Existing approaches to achieve
fairness, a type of preference, in RL typically assume that such preferences are known a priori and require complete retraining of the policy under a
fairness-oriented metric. Inspired by inference-time
alignment in large
language models, we investigate the problem of steering a pretrained RL policy toward welfare-based
fairness objectives at inference time without updating the base policy's parameters. We formalize inference-time
fairness alignment as a policy shaping problem and propose a multiplicative policy shaping framework that adjusts action probabilities using action-dependent welfare scores, thus requiring no modification to the base policy. Our framework is general and compatible with any deep RL
agent. Through extensive experiments across multiple domains, we demonstrate that inference-time policy shaping substantially improves welfare-based
fairness objectives while preserving core task performance.
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All authors Umer Siddique, Peilang Li, Conor Wallace, Yongcan Cao
Artificial Intelligence
AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?
Dong Yan, Jian Liang, Dapeng Hu et al. (+4 more)
Large
language model (LLM)
agents can self-evolve by continually improving from their own accumulated experience. However, existing studies predominantly adopt independent evaluation. Consequently, the behavior of self-evolving
agents in realistic streaming settings, where
agents adapt to diverse and complex task streams, remains poorly understood. To address this gap, we introduce AgentStream, a unified framework that evaluates self-evolving
agents spanning diverse
evolution components by organizing agentic
benchmarks into a configurable task stream and instantiating the Isolated, Sequential, and Interleaved streaming scenarios at test time, which progressively vary the scope and domain composition of the stream. Over these scenarios, we combinatorially evaluate five representative self-evolving methods across three frontier foundation models, disentangling how model capability, method architecture, and streaming scenario jointly shape self-
evolution. Our results show that self-
evolution reliability varies across streaming scenarios, the benefit of self-
evolution is gated by model capability and is non-monotonic in model strength, and no single method dominates across models and scenarios. These findings offer concrete guidance for selecting self-evolving methods across models and streaming scenarios. Overall, we advocate that self-evolving
agents should be evaluated under realistic task streams rather than isolated single-task settings.
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All authors Dong Yan, Jian Liang, Dapeng Hu, Ran He, Nicholas Jing Yuan, Qi Zhang, Tieniu Tan
Artificial Intelligence · Cross-listed from cs.LG
Can We Trust In-Distribution Success? Locked Evaluation Reveals Transfer Failure and Sampling-Depth Entanglement in CRISPRi Perturbation Prediction
Mehrdad Shoeibi and Niloofar Yousefi
AI evaluation can support the wrong inference when an in-domain
benchmark success does not survive distribution shift, or when the
benchmark endpoint is entangled with a design factor. We study this problem in CRISPRi perturbation-effect prediction, evaluating a frozen Geneformer representation under a locked, pre-registered protocol: heads and model selection were frozen before test evaluation; the protocol required external outcome labels to remain withheld until final unblinding; and analysis-governing decisions were fixed before the evaluations they govern. In-distribution on the Virtual
Cell Challenge (VCC), the frozen representation carries measurable predictive information beyond a dimension-matched random-feature control (Delta R^2 = +0.1645, 95% CI [+0.1375, +0.1920]), satisfying the pre-registered informativeness gate required before interpreting transfer. It then fails zero-shot transfer on both external screens (Spearman rho = -0.139 and -0.267), lying below that control on each. Adding a predefined magnitude block improves the representation externally (Delta rho = +0.032 and +0.143) but, under the frozen primary head, does not rescue transfer: both remain negative. A pre-registered, count-adjusted max-response secondary is positively associated with the outcome on both screens; we report it as correlational and secondary, not as a recovered magnitude signal. Finally, the VCC endpoint is strongly sample-size associated: a count-only linear model reaches R^2 = +0.4325, versus +0.2589 for the four magnitude scalars; adding those scalars to
cell count improves R^2 by only +0.0017, so much of the aggregate-magnitude signal overlaps with
cell count. This case study shows how locking the evaluation, harmonizing the measured endpoint, and separating primary from secondary evidence can change the inference supported by an AI
benchmark.
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Artificial Intelligence · Cross-listed from cs.CY
Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systems
Keyun Ruan, Jonathan D. Teubner and John M. Bremen
Current evaluation frameworks for artificial intelligence focus mainly on capability, safety, and proxies such as adoption, engagement, efficiency, productivity, and financial return. These criteria are necessary but insufficient because they do not establish whether increasingly powerful systems improve or degrade human and planetary well-being. Through an integrative conceptual synthesis, we argue that human flourishing should serve as a primary success criterion for artificial intelligence, the global race to develop increasingly capable AI systems, and prospective post-AGI economic systems. We make three contributions. First, Flourishing Metrics provides an extensible framework spanning physical, emotional, financial, relational, spiritual, and planetary well-being, combining validated subjective measures with representative behavioural, organisational, community, and environmental indicators. Second, Return on Flourishing (RoF) extends return on investment by evaluating the counterfactual contribution of interventions, policies, and AI systems to flourishing relative to their resources, risks, and opportunity costs. Third, we develop distribution-sensitive safeguards and show how RoF could guide AI-enabled work redesign, institutional appraisal, assurance, and post-deployment monitoring through business pilots. We formalise flourishing as a dynamic system variable while emphasising the need for democratic specification, empirical calibration, independent validation, and protection against unacceptable losses within particular dimensions or stakeholder groups. RoF is proposed not as a universal reward function, but as a value-accounting and decision architecture for assessing whether intelligence, automation, and economic transformation generate durable human and planetary progress.
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Artificial Intelligence · Cross-listed from cs.CR
Exposed by Design: A Dynamic Security Assessment of Internet-Facing MCP Servers at Scale
Nicolás Padilla
The Model Context Protocol (MCP) has seen rapid adoption since its November 2024 launch, with over 21,000 server instances detectable on the public internet. We present the first dynamic behavioral security assessment of internet-facing MCP servers, combining passive discovery across eleven data sources (crt.sh, HuggingFace, GitHub, npm, Smithery, PyPI, Censys, FOFA, Shodan, glama.ai, and pulsemcp.com) with active dynamic testing using Corvus, a purpose-built framework implementing 34 test modules covering 10 MCP-specific vulnerability classes. Across four measurement runs spanning July 2026, we confirm 640 production MCP servers and dynamically audit 414, uncovering 68 reportable vulnerabilities including SQL injection, SSRF targeting cloud metadata services,
prompt template injection, and path traversal via cursor manipulation. We find that 91.8% of dynamically audited servers lack OAuth authentication, 687 tool instances across confirmed servers expose shell execution capabilities without access controls, and 41.6% of confirmed servers disappear within three days between consecutive measurement runs—indicating rapid deployment cycles without security review. We report on our responsible disclosure pipeline and release Corvus as an open-source framework for MCP security evaluation.
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Artificial Intelligence · Cross-listed from cond-mat.soft
A Synthetically-accessible Universe of Chemically Recyclable Polymers
Anagha Savit, Wei Xiong, Harikrishna Sahu et al. (+3 more)
Polymers synthesized via ring-opening polymerization (ROP) of cyclic monomers represent an important class of materials due to their chemical recyclability and possible insertion in several critical applications. We present a
dataset of 1 million synthetically realizable ROP
polymer structures generated through a combination of Virtual Forward Synthesis (VFS) and
polymer expert
language models and qualified by stringent chemical heuristics. VFS is used to generate ROP
polymers by applying known reactions to existing monomers. The
polymer foundation models polyBART and POLYT5 further enable the generation of ROP candidates, with polyBART exploring its learned latent space and POLYT5 producing candidates via sequence-to-sequence generation. The resulting ROP
polymers are subjected to robust filtering criteria to ensure novelty, validity and overall data quality through a combination of automated validation pipelines and a comprehensive set of chemist-informed heuristic rules introduced in this work for the first time. We hope that this
dataset will serve as a valuable resource for downstream sustainable applications.
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All authors Anagha Savit, Wei Xiong, Harikrishna Sahu, Shivank S. Shukla, Will R. Gutekunst, Rampi Ramprasad
Artificial Intelligence · Cross-listed from cs.CL
DiffusionGemma Technical Report
DiffusionGemma Team: Adrien Ali Taïga, James Assiene, Daniele Calandriello et al. (+40 more)
We introduce DiffusionGemma, an experimental open-weight
language model that uses discrete
diffusion to generate text at exceptionally high speed. Rather than decoding one
token at a time, DiffusionGemma iteratively refines blocks of 256
tokens in parallel, avoiding the sequential decoding bottleneck of conventional autoregressive (AR) large
language models. Instead of training from scratch, we obtain DiffusionGemma by fine-tuning the mixture-of-experts Gemma 4 model with 3.8B activated and 25.2B total parameters. Our compute-efficient two-stage training pipeline uses fewer than 10% of the starting AR model's total training
token budget. The first stage uses supervised fine-tuning to teach bidirectional denoising, while the second stage combines reinforcement learning with sampler
distillation to jointly improve generation quality and inference efficiency. DiffusionGemma establishes a new Pareto frontier for the trade-off between generation speed and model capability. Averaged across our full evaluation suite, it generates around 20
tokens per forward pass and achieves roughly 1,500 output
tokens per second on a single NVIDIA H100 GPU, which is substantially faster than AR models even with state-of-the-art speculative decoding. DiffusionGemma also retains the starting model's support for thinking mode,
multimodal inputs, and long contexts. Despite
diffusion fine-tuning, it remains capable of AR generation with only minor performance degradation, suggesting a path toward hybrid
diffusion-AR decoding.
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All authors DiffusionGemma Team: Adrien Ali Taïga, James Assiene, Daniele Calandriello, Rahma Chaabouni, João Gante, Tamara von Glehn, Nate Keating, Chris Knutsen, Martin Kukla, Tianlin Liu, Ivan Lobov, Ofir Nabati, João Gabriel Oliveira, Nicolas Perez-Nieves, Nastasia Prutianova, Bobak Shahriari, Jean Tarbouriech, Pavel Tyletski, Çağlar Ünlü, Cindy Wu, Glenn Cameron, Jerome Connor, Sertan Girgin, Maarten Grootendorst, Alon Levkovitch, Eliya Nachmani, Omar Sanseviero, Piotr Stanczyk, Quentin Berthet, Andrew Campbell, Clément Crepy, Valentin De Bortoli, Arnaud Doucet, Romuald Elie, Alexandre Galashov, Klaus Greff, Alexis Jacq, David Ruhe, Yu-Han Wu, Sebastian Flennerhag, Brendan O'Donoghue, George Scrivener, Shantanu Thakoor
Artificial Intelligence · Cross-listed from cs.CR
Symbolic Attack Chain Generation from Atomic Red Team Techniques: An Empirical Study of Predicate Representation Granularity
Ramya Varunsegar
Automated attack chain generation is critical for modern cybersecurity, yet manual construction fails to scale as adversary behaviors expand. While classical AI planning using the Planning Domain Definition
Language (PDDL) offers a formal method to automate this process, it relies on the accurate translation of techniques into symbolic predicates. Current state-of-the-art systems like AURORA employ a nine-category Attack Action Linking Model (AALM), but the necessity of this specific granularity remains unvalidated. This work investigates whether AURORA's nine-category taxonomy provides representational distinctions beyond those captured by a reduced, empirically derived scheme. Utilizing a pipeline where a Large
Language Model (LLM) performs translation and the Fast Downward engine performs deterministic
reasoning, the study compares the full nine-category AALM against a reduced five-category scheme derived empirically from Atomic Red Team (ART) execution evidence. Because the nine-category domain is constructed as a relabeling of the five-category domain, plan validity and cost are held identical between schemes by design; the substantive test of granularity's effect lies instead in the resulting predicate category resolution. There, a controlled A/B test isolates a case where a coarser scheme's plan passes every validity check while remaining operationally wrong: holding administrator privilege and being able to exercise it over a network logon prove to be causally distinct system states. Results from a sixteen-technique corpus show 81.3% identical plan outcomes across both schemes by construction, with a genuine predicate category resolution gain confined to a single technique out of sixteen. The findings suggest that higher granularity primarily enhances the internal structural resolution of a plan's justification rather than the viability of the generated attack chain itself.
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