Artificial Intelligence · Cross-listed from q-bio.OT
Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration
Sylvain V. Costes, Sergio Garcia Busto, Ryan T. Scott et al. (+15 more)
While AI holds the potential to revolutionize space life sciences, realizing this promise is contingent upon the systematic restructuring of heterogeneous spaceflight biological data into machine-actionable, AI-ready forms. Even though open access principles support human reuse and scientific reproducibility, this does not necessarily enable AI systems to access and analyze such a diverse set of scientific
datasets. In addition, the growing array of AI approaches places distinct demands on data structure, metadata, and access interfaces. In order to respond to such growing changes we propose a three-tier approach, proceeding from FAIR to AI-ready to space-ready data. We discuss existing infrastructures and how they can be improved to close the AI access gap. We conclude by proposing a neutral international coordinating body as the governance backbone for the trustworthy,
agent-accessible space biology infrastructure that deep space biological research will require.
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All authors Sylvain V. Costes, Sergio Garcia Busto, Ryan T. Scott, James A. Casaletto, Gautier Bardi de Fourtou, Brian M. Evarts, Amanda M. Saravia-Butler, Xavier-Lewis Palmer, Rodrigo Coutinho de Almeida, Laetitia Frost, Jelena Tešić, Afshin Beheshti, Christopher E. Mason, Peter W. Rose, Sergio E. Baranzini, Lauren M. Sanders, Stefania Giacomello, Pedro Madrigal
Artificial Intelligence · Cross-listed from stat.ML
Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation
Carel F.W. Peeters
The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a single-hidden-layer generative neural network. We thus consider it a basal generative representation learner that can be used as a minimal model for studying the foundational characteristics of (deep) generative model architectures. We focus on the fundamental problem of indeterminacy in latent factor projections. This indeterminacy implies that, even when the intrinsic dimension of the latent vector is known, regularity conditions are met, and rotational indeterminacy is resolved, an inherent indefiniteness in the
retrieval of causative latent sources remains: they will be uncertain, distributionally deviant, and non-unique. This can have major implications for data representation but remains an elusive issue, even to practitioners and theorists well-versed in the factor model. Moreover, this classic psychometric problem is intricately related to the modern issue of latent variable collapse in the variational autoencoder framework for deep generative modeling. Here, we assess this indeterminacy from various perspectives and show how these are mathematically and conceptually related and we discuss subsequent implications for the Psychometrics, Statistics, and Artificial Intelligence communities. We show that one has latent factor determinacy across all its facets when the feature-dimension grows to infinity. This feeds into an essentially distribution-free estimation approach in the sample case when the number of features grows very large. We conclude, as these are emergent properties at scale, that the factor model is suited for representation learning of very-high-dimensional data.
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Artificial Intelligence · Cross-listed from cs.CL
The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm et al. (+8 more)
Fine-tuning a large
language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown that this increase in capability comes with a cost: it can increase a model's tendency to respond to unsafe
adversarial prompts, even when fine-tuning with non-
adversarial data. We present the first comprehensive empirical study of this phenomenon in
multilingual settings by fine-tuning Llama-3.2, Qwen3, and Gemma-3 models using benign data translated across nine
languages. We find that safety outcomes are highly sensitive to both the choice of fine-tuning
language and the evaluation
language, with
adversarial compliance rates increasing four-fold in some settings.
Multilingual safety drift is decoupled from general capability metrics, and occurs heterogeneously across
languages and models. Fine-tuning in non-English
languages often induces smaller internal representational drifts than English, but these shifts lead models to default to either exaggerated compliance or refusal. As such, assessing fine-tuning impacts solely in English provides inadequate assurance for deployment. To facilitate further research into these cross-lingual safety blind spots, we release the
Multilingual-Benign-Tune
dataset and the SORRY-Bench-
Multilingual evaluation suite.
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All authors Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm, Stratis Tsirtsis, Zihao Fu, Greta Warren, Ryan Brown, Eoin Delaney, Sandra Wachter, Brent Mittelstadt, Chris Russell
Artificial Intelligence · Cross-listed from cs.LO
LAMP: Lean-based Agentic framework with MCP and Proof Repair
Santhana Srinivasan R and Maithilee Patawar
Large
language models are increasingly capable of mathematical
reasoning, but the proofs they generate are often unreliable and hard to verify. Interactive
theorem provers such as Lean 4 address this by accepting only kernel-checked proofs; however, their reach is bounded by the formalized knowledge available. While Mathlib, a repository of formalized Lean 4
theorems that covers diverse mathematical areas, certain specialized areas remain underrepresented; notably, the domain of Combinatorics on Words (CoW). CoW studies sequences, exploring their properties such as periodicity, borders, conjugacy, and morphisms. As a result, specialized provers, trained on Mathlib-centered data, lack the lemmas to operate in CoW. We present two contributions. First, we introduce a Lean 4 formalization of CoW containing eight modules and 93 declarations of core definitions and foundational lemmas. Second, we present LAMP, a multi-
agent framework that synthesizes kernel-verified Lean 4 proofs by providing explicit, structured domain knowledge at inference time through an ontology, rather than by fine-tuning a prover. LAMP coordinates a Planner, Builder, and Verifier with Model Context Protocol based access to a domain-specific CoW ontology. In a suite of 90 CoW
theorems that span all eight modules and three difficulty levels, LAMP synthesizes verified proofs for 96.7% of
theorems, substantially exceeding both an unscaffolded baseline and existing specialized provers. An ablation shows that removing LAMP's tool-grounded architecture or its Planner/Builder separation each cost roughly 12 percentage points, even with the backbone model held fixed.
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Artificial Intelligence · Cross-listed from cs.LG
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Wenhao Yuan, Chenchen Lin, Jian Chen et al. (+3 more)
Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges: 1) Capacity conflict and catastrophic forgetting from the shared model overloading, 2) Heterogeneity from Non-Independent and Identically Distributed (Non-IID) data, and 3) Synchronized class misalignment. In this paper, we propose Fisher-Routed MiXture of Experts for
Federated Class-Incremental Learning (FedFMX), a novel framework to address these challenges via adaptive expert specialization across clients. The crucial insight is to route each sample to an expert subset that jointly optimizes knowledge acquisition and retention. Specifically, we introduce a Fisher-Routed Expert Scoring (FRES) module to estimate expert importance via Fisher-based stability cost and gradient-based plasticity gain. Then, we design an Adaptive Expert Selection (AES) module by quantifying marginal contributions for adaptive expert subset determination. Finally, by the routing-aware regularization (RAR), we achieve load balance and efficient FL training. We theoretically prove the $\mathcal{O}(T^{-1})$ convergence rate. Extensive experiments on multiple
benchmarks compared with state-of-the-art methods demonstrate the superiority of FedFMX.
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All authors Wenhao Yuan, Chenchen Lin, Jian Chen, Jinfeng Xu, Zewei Liu, Edith Cheuk Han Ngai
Artificial Intelligence · Cross-listed from cs.LG
HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression
Yuxuan Yang, Feiyang Ren, Bowen Zeng et al. (+4 more)
Long-context LLM inference faces a fundamental conflict: head-adaptive compression
algorithms (e.g., Top-$p$
nucleus sampling) offer superior accuracy by dynamically fluctuating
memory budgets, yet modern inference engines (e.g., vLLM) demand rigid, static
memory patterns to leverage CUDA Graphs and PagedAttention. We resolve this “Static-Dynamic” mismatch with HARD-KV, a unified framework that that bridges dynamic selection with rigid system constraints. HARD-KV introduces a Cascade Cache hierarchy, managing the
token lifecycle across dense, sparse, and condensed tiers. Crucially, we propose a Logits Calibration mechanism that normalizes diverse importance metrics into a unified probability space, enabling consistent Top-$p$ budgeting across heterogeneous heads. To bridge the efficiency gap, we offer a system-level solution, which rewrites fragmented, dynamic indices into contiguous physical layouts compatible with high-performance inference engine. Extensive experiments on math-
reasoning benchmarks (AIME, U-Math) verify that HARD-KV achieves up to 2$\times$ throughput improvement over static baselines while maintaining high-fidelity generation in 10k+
token scenarios. Code is available at https://github.com/SuDIS-ZJU/HARDInfer.
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All authors Yuxuan Yang, Feiyang Ren, Bowen Zeng, Dalin Zhang, Jinpeng Chen, Gang Chen, Huan Li
Artificial Intelligence · Cross-listed from cs.GT
Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games
Quanyan Zhu
This paper develops a continuum theory of exit-and-join coalition dynamics in nonatomic cooperative games. We extend the Aumann-Shapley value and the Aumann-Drèze value to coalition structures in which each coalition is treated as a restricted nonatomic game, yielding a marginal-contribution-based payoff density that governs incentives for
agents to remain in, exit, or join coalitions. We derive deterministic mean-field dynamics from decentralized switching rules and show that payoff-difference switching recovers replicator dynamics as a special case. We characterize exit-and-join
equilibrium by the absence of profitable positive-mass deviations and prove its equivalence with stationarity of the induced mass dynamics under incentive-compatible and strictly payoff-responsive switching rates. For mass-based cooperative games, we construct a Lyapunov function and establish global convergence under strict concavity. We further show that the
equilibrium is equivalent to a Wardrop
equilibrium of an induced nonatomic population game and admits a variational inequality formulation. The framework is extended to incorporate switching costs and endogenous coalition acceptance rules, leading to constrained
equilibria characterized by quasi-variational inequalities. The proposed theory unifies cooperative value allocation, noncooperative coalition mobility, mean-field dynamics, evolutionary game theory, and population games within a common framework for analyzing coalition formation and adaptation in large-scale multi-
agent systems.
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Artificial Intelligence · Cross-listed from cs.DL
Categorizing Mathematical Concepts with LLM Voting Ensembles in Mathswitch
Katja Berčič and Slobodan Stanojevikj
Mathswitch is an open-source project that imports mathematical concept records from sources such as Wikidata, Wikipedia, MathWorld, Encyclopedia of Mathematics, nLab, ProofWiki, and Agda-Unimath, and links records that refer to the same concept. It does not reorganize or redefine the imported content; each source retains its own structure. The current focus is on importing concept data from Wikidata and the resources it links to, with plans to expand to further sources and better concept linking. Because the concept set is approximated through queries over Wikidata's collaboratively edited graph, the imported data is noisy: some items are non-mathematical, while others are ambiguous. In this paper, we test whether a voting ensemble of LLM judges can filter this noise. We evaluate it on Wikidata items with known MathWorld identifiers as a positive control, and examine how classification changes when database identifiers are removed from context. We then inspect the cases where the judges disagree with MathWorld and group these disagreements into three categories (degenerate descriptions, narrow scope bias, and editorial-scope mismatches) that suggest different remediation strategies.
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Artificial Intelligence · Cross-listed from cs.RO
Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
Shuo Cheng, Chuye Zhang, Alfred Cueva et al. (+3 more)
Human
videos are a scalable source of supervision for
robot manipulation, as they are abundant and naturally capture rich object interactions. However, transferring human demonstrations to
robots remains challenging due to embodiment mismatch, scene variation, and
robot-specific feasibility constraints. We present Human2Any, a framework for learning reusable object-centric interaction priors from human
videos without requiring real-world
robot demonstrations in the target task contexts. Human2Any represents manipulation through object-object interaction motion, capturing task-relevant scene changes while abstracting away embodiment-specific details. It composes learned interaction priors with
robot-side feasibility
reasoning and motion planning, allowing the same human-derived knowledge to adapt to different embodiments, scene geometries, and task contexts. We validate Human2Any across diverse manipulation settings, including real-world experiments on a Franka tabletop setup and an RBY-1
humanoid mobile
robot, demonstrating robust interaction-centric manipulation without real-world
robot training data. Project website: https://human2any.github.io/.
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All authors Shuo Cheng, Chuye Zhang, Alfred Cueva, Caelan Garrett, Ajay Mandlekar, Danfei Xu
Artificial Intelligence
A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data
Chengyuan Liu, Xinyue Zhang, Yao Li et al. (+1 more)
The main objective of this paper is to propose a general framework for prediction based on different sources of
multimodal data in the healthcare domain. We evaluated whether frozen medical large
language model (LLM) representations can serve as a shared
embedding space for
multimodal primary diagnosis category prediction. We propose a backbone-flexible probing framework that serializes structured electronic health record (EHR) variables and leakage-pruned discharge-note sections into a shared frozen-LLM representation space and recovers primary diagnosis categories with linear probes, without fine-tuning the LLM. On a MIMIC-IV cohort of 13,645 admissions (seven diagnosis categories), we probed inputs of different modalities (Structured-only, Unstructured-only, and Combined) across
transformer layers and six frozen backbones, compared against XGBoost and three text baselines (CAML, LAAT, PLM-ICD) under a matched split and context budget, and tested robustness across
datasets on MIMIC-III. The Combined terminal-layer probe reached 88.86% top-1 accuracy on MIMIC-IV, significantly outperforming CAML and PLM-ICD and statistically comparable to LAAT; Combined accuracy ranged from 87.69% to 89.25% across six backbones. On MIMIC-III, in-domain Combined probing reached 74.18% accuracy and significantly exceeded all three baselines, and in a transfer-learning test a 2.8M-parameter adapter on frozen MIMIC-IV representations reached 92.51% accuracy, exceeding adapted LAAT (85.54%). Frozen LLM
embeddings unify structured and narrative EHR information: the Combined configuration was strongest in every setting, and its representations transferred across
datasets through a compact adapter.
Multimodal probing of frozen LLM representations provides a practical, backbone-flexible approach for studying EHR modalities and adapting clinical representations across
datasets.
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All authors Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen
Artificial Intelligence · Cross-listed from cs.CY
The registrar's function in a hybrid society. AI value chain,smart data and the concept of property
Pompeu Casanovas, Carmen Pastor Sempere and Marina Echebarria Saenz
Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into economic value. This paper argues that the decisive legal move is to place validity, a functional, second-order concept, at the centre of that chain. Rights, liability and supervision organise around it. It traces three impacts.Registry information becomes smart data, governed simultaneously by registry law, the GDPR, the European data acts and the AI Act. Control emerges as the operative concept for digital representations of real estate, whose proprietary effect depends on anchoring to the register. In a hybrid society of human and artificial
agents, the registry becomes the public node of validity, with blockchain complementing rather than replacing it. Across three legal cultures, the registra's value migrates from processing documents to guaranteeing validated data,making validity an asset for the UNO Sustainable Development Goals.
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Artificial Intelligence · Cross-listed from cs.CV
BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT
Djahid Abdelmoumene, Ishak Ayad, Maï K. Nguyen et al. (+1 more)
Multi-Frequency Electrical Impedance
Tomography (MF-EIT) is a non-invasive, low-cost modality that reconstructs electrical property distributions from boundary voltages. For stroke imaging, progress in 3D deep-learning reconstruction is limited by the lack of large-scale
datasets with paired ground-truth (GT) volumes and by non-standardized pipelines for data generation, simulation, and evaluation. We introduce BREIT, a modular framework for 3D MF-EIT stroke reconstruction providing: (i) a neuroimaging-to-EIT pipeline that converts CT/MRI into frequency-dependent GT admittivity volumes; (ii) a self-contained Python 3D Complete Electrode Model (CEM) forward solver for simulating MF-EIT voltages; and (iii) a 3D D-bar implementation supporting non-uniform electrode layouts. Building on BREIT, we propose dFNO-bar, which integrates
Fourier Neural Operators into D-bar by learning a mapping from scattering data $t(\xi)$ to conductivity $\sigma(x){=}\Re\{\gamma\}$. We evaluate dFNO-bar against D-bar, Deep D-bar, and Gauss–Newton reconstructions on UCLH-matched synthetic data, and observe higher
brain SSIM with comparable CC across noise settings.
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All authors Djahid Abdelmoumene, Ishak Ayad, Maï K. Nguyen, Christian Daveau
Artificial Intelligence
HyphaeDB: A Living Knowledge Topology for Agent-First Memory
Krishna Halaharvi
Every existing vector database and
agent memory framework treats
memory as passive storage that
agents query explicitly. No system propagates knowledge between
agents through the
memory layer itself. We introduce HyphaeDB, an
agent-native
memory infrastructure that reinterprets the Hierarchical Navigable Small World (HNSW) graph
topology the data structure at the core of every modern vector database not as a search optimization, but as a communication fabric for multi-
agent AI systems. In HyphaeDB,
agents are nodes in the vector space with persistent positions, knowledge propagates via a gossip protocol through the graph's neighbor structure with energy-based attenuation, and emergent behaviors contradiction detection, pattern crystallization, and consensus formation arise from the combination of
topology, propagation dynamics, and local interaction rules. We present the architecture built on three primitives (knowledge nodes,
topology edges, and
memory diffs), a multi-layer abstraction hierarchy with promotion via emergent consensus, and theoretical analysis grounding the system in small-world network theory, epidemic broadcast protocols, and swarm intelligence. We provide a reference implementation on PostgreSQL with pgvector and describe a concrete deployment in Swarm-Driven Development, a multi-
agent software engineering methodology. HyphaeDB represents, to our knowledge, the first system to combine navigable small world
topology with gossip-based knowledge propagation for multi-
agent coordination.
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Artificial Intelligence · Cross-listed from cs.IT
Brownian Bridge Diffusion-Based Joint Channel Estimation and Data Detection for Jamming-Resilient Receivers
Honghan She, Yufan Cheng, Tieming Sun et al. (+3 more)
In next-generation wireless networks, the growing density of devices and limited spectrum resources pose severe jamming challenges to fragile legitimate communication links in the wireless electromagnetic environment. Crucially, when jamming overlaps with pilot and data symbols in both time and frequency domains, it inflicts a severe bottleneck on receiver-side joint estimation and detection. Existing schemes often lack an effective framework to combat such jamming contamination, thereby failing to guarantee reliable transmission. To address this issue, we propose a Brownian bridge
diffusion-based joint channel estimation and data detection framework (BBD-JCED) for jamming-resilient receivers. Specifically, the proposed framework comprises two core modules: the first extracts jamming features in the short-time
Fourier transform (STFT) domain and suppresses jamming samples, thereby improving the signal-to-jamming-plus-noise ratio (SJNR) of the received signal; the second introduces a Brownian bridge
diffusion (BBD) process to model the
evolution of the suppressed signal and the encoded bits in the presence of channel estimation errors, thereby enabling enhanced joint channel estimation and data detection. To alleviate the computational burden of the BBD process in the second module, we further derive a fast ordinary differential equation (ODE) solver that enables its low-complexity iterative
evolution. Finally, we design a multi-module training
algorithm to improve the data recovery capability of the proposed framework. Simulation results demonstrate that the proposed framework achieves superior bit recovery performance compared with baseline schemes while maintaining a lower number of model parameters and competitive computational complexity.
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All authors Honghan She, Yufan Cheng, Tieming Sun, Pengyu Wang, Siya Huang, Kaikai Yang
Artificial Intelligence · Cross-listed from cs.CL
Majority Vote Silences Minority Values: Annotator Disagreement at the Hate/Offensive Boundary in HateXplain
Joshua Muhumuza, Joab Ezra Agaba and Mercy Amiyo
Hate
speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training. We show that this aggregation is not neutral: 42.6% of all annotator disagreement in HateXplain concentrates specifically at the hate/offensive boundary, a pattern consistent with annotators applying different thresholds for where hate begins (chi-squared = 135.199, df = 2, p < 0.0001). Both a hard-label BERT model (Model A) and a soft-label model (Model B) drop 22 percentage points in accuracy from agreed posts (~80%) to disagreement posts (~58%), confirmed at p < 0.0001. A per-annotator multi-head model (Model C) widens this gap further to 28 points while collapsing offensive disagreement accuracy to 0.245. Critically, Model A expresses significantly higher confidence on boundary case errors than Model C (0.710 vs. 0.495, p < 0.0001), meaning standard evaluation metrics will not detect the failure. Three downstream interventions of increasing sophistication all fail to recover boundary accuracy. We argue the problem is structural. Majority vote presents a contested judgment as ground truth, and models inherit that false certainty. The intervention must be upstream in annotation design.
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Artificial Intelligence
Mechanistic Personality Analysis of LLMs: Steering Personality via Latent Feature Interventions
David Courtis and Ting Hu
Large
Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text. Previous efforts have focused on
prompt engineering or fine-tuning to shape LLM personality. In this work, we propose a mechanistic interpretability approach that directly intervenes on the model's latent features. Our method identifies latent directions in the residual stream corresponding to a target OCEAN trait using sparse autoencoders (SAEs) and contrastive activation analysis. We formalize an additive steering vector in activation space and demonstrate how applying a small additive shift to the hidden states enhances the target trait while preserving overall
language modeling performance. To determine the optimal combination of feature shifts, we explore a linear weighting heuristic with grid search optimization that balances personality expression with task performance. Our approach shows promise in controllably steering personality traits at the mechanistic level while maintaining high performance on standard
benchmarks.
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Artificial Intelligence · Cross-listed from cs.CV
X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving
Bohao Zhao, Chengrui Wei, Guangfeng Jiang et al. (+17 more)
Predicting future states is essential for autonomous
agents, yet current
Vision-
Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive perception-action mapping. While integrating Predictive World Models (PWMs) addresses this gap, existing approaches either incur prohibitive cascaded latency or act as shallow terminal tasks that fail to deeply embed forward-looking
reasoning. To endow VLA models with this
reasoning capability, we propose X-Mind. Rather than treating PWMs as an external auxiliary module, this framework internalizes them as the Visual Chain-of-Thought (Visual CoT). By enforcing a world rollout prior to action, the model is constrained to imagine future
evolution first, yielding a driving policy that is robustly grounded in environmental dynamics and aware of the future consequences its actions will unfold. The challenge here is efficiency, and we tackle it on two fronts. First, we introduce a compact representation of visual thinking: an abstract sketch that fuses a Bird's-Eye-View (BEV) layout with abstract driving priors (e.g.,
navigation intents and traffic rules). Rather than rolling out dense future frames, the model reasons over this sketch as a mental canvas; aided by a Deep Compression Autoencoder (DC-AE), a 12-frame future rollout is reduced to merely 96
tokens, alleviating the long-context computational bottleneck. Second, to accelerate generation further, we propose a recurrent block
diffusion scheme that unrolls the denoising steps across the layers of the large drive model, folding iterative refinement into the backbone's one forward pass. Trained and validated on large-scale real-world data, X-Mind achieves competitive end-to-end driving performance, which makes it a highly practical, low-latency solution that successfully deploys large-scale cognitive
reasoning directly onto resource-constrained vehicle platforms.
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All authors Bohao Zhao, Chengrui Wei, Guangfeng Jiang, Ruixin Liu, Xuejie Lv, Liu Liang, Sutao Deng, Xiuyang Fan, Pengkun Zheng, Jinyun Zhou, Rui Guo, Hanpeng Liu, Yutong Zheng, Yi Guo, Xinlong Zheng, Qingyu Luo, Zhuangzhuang Ding, Yu Zhang, Hang Zhang, Xianming Liu
Artificial Intelligence · Cross-listed from cs.CY
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
Shahin Hossain
Although most undergraduates now use large
language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them. Existing instruments assess reliance solely by frequency of use, a measure that, as this study shows, inadvertently rewards dependence on AI rather than recognizing students' own intellectual contribution. Conducted at a public minority-serving university and grounded in the AI Literacy Framework, Expectancy-Value Theory, and Biggs's Presage-Process-Product model, the study drew on 382 undergraduates, 14 interviews, and 396 open-ended survey responses. Four distinct reliance types were identified and confirmed: Strategic (34.3%), Instrumental (30.9%), Dialogic (30.4%), and Dependent (4.5%). Students' value and cost beliefs predicted the intensity of their reliance on LLMs, whereas their AI literacy predicted the type of reliance they adopted, indicating that differentiated support is needed. Notably, Strategic users, those who engaged AI most deliberately, scored lowest on standard outcome measures. This pattern reflects a limitation of current instruments, which index AI's contribution rather than writing quality, thereby penalizing students who show the greatest independent thinking. Analysis also revealed an additional group, roughly 13%, who declined to use AI for ethical rather than practical reasons, and who existing frameworks overlook. These findings carry implications for AI literacy programs, the measurement of student learning outcomes, and equitable AI policy at minority-serving institutions.
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Artificial Intelligence
Self-Supervised Theorem Discovery in a Formal Axiomatic System
Kazuki Ota, Takayuki Osa and Tatsuya Harada
Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical
reasoning. Many existing approaches, including large
language models (LLMs), draw on human prior knowledge in the form of mathematical text, code, or
theorem libraries. Although these approaches are highly effective in practice, it remains an open question whether an
agent can autonomously discover useful
theorems without such human priors. We study this question in a formal axiomatic system by developing an
agent that starts from axioms and inference rules alone and gradually grows a library of useful
theorems. Concretely, we propose a self-supervised
theorem-discovery
algorithm that alternates between proof search and useful-
theorem extraction, building a
theorem library whose entries are reused as lemmas for subsequent proof search. Experiments show that the
agent discovers tens of thousands of
theorems and finds proofs for human-written
benchmark problems, suggesting that its discoveries include
theorems meaningful from a human mathematical perspective. Furthermore, the discovered
theorems improve LLM proof performance when provided as
prompt lemmas, indicating that they can serve as external knowledge for LLM
reasoning. Our results provide evidence that useful
theorems can emerge from proof search without relying on human-provided
theorem libraries. More broadly, they suggest a path toward self-evolving AI systems for mathematics whose discoveries remain formally verifiable.
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Artificial Intelligence
Shawn Li and Yue Zhao
Large
language models increasingly act as
agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the chatbot-era recipe (train the model to refuse unsafe inputs) into the agentic setting, and treat the resulting capability loss as a manageable “
alignment tax.” We argue this is a category error. Refusal is a primitive for content safety, where the harm is in the model's output and is therefore a learnable function of it. Agentic harm is different in kind: it lies not in any output but in the relation between the authority an action exercises and the authority the user granted, which is absent from the text the model sees. Importing content-safety methods into this regime does not trade capability for safety; it pays capability and buys negative security. We support this with three lines of evidence spanning the autonomy spectrum: defense-trained models learn surface patterns rather than intent; the same training collapses multi-step
agents before any threat appears while leaving them exploitable; and even undefended frontier models exceed granted authority under ordinary use. We conclude that action safety cannot be installed in weights. It must be expressed as least privilege, enforced outside the model at the action boundary, and evaluated as action
alignment (a relational, deployment-conditioned property) rather than a refusal score.
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Artificial Intelligence · Cross-listed from cs.CL
5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control
Thien-Qua-T-Nguyen, Chi Hoang, Nguyen Tran et al. (+3 more)
We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn
Retrieval Augmented Generation (RAG) systems. Multi turn RAG involves context drift, under specification, and
hallucination risk. Our system combines BGE-M3 dense
retrieval with FAISS indexing, dual-query merged
retrieval, and LLM based reranking, followed by role separated generation constrained to retrieved evidence. The retriever achieved nDCG@5 = 0.4719 in Task A, while the end to end system ranked in Task C with a harmonic score of 0.5597 and RL_F = 0.7692.
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All authors Thien-Qua-T-Nguyen, Chi Hoang, Nguyen Tran, Tri Le, Khanh Truong, Chinh Trong Nguyen
Artificial Intelligence
Agentic Abstention: Do Agents Know When to Stop Instead of Act?
Han Luo, Bingbing Wen and Lucy Lu Wang
LLM
agents are expected to act over multiple turns, using search, browsing interfaces, and tools to complete user goals. Yet not every goal is well specified or achievable in an available environment. In such cases, a reliable
agent should recognize that further interaction is unlikely to help and abstain from additional tool calls. We define Agentic Abstention, the problem of deciding when an
agent should stop acting under uncertainty. Unlike standard LLM abstention, which is usually evaluated as a single-turn answer-or-abstain decision, agentic abstention is a sequential decision problem: an
agent can answer, abstain, or gather more information at each turn, and the need to abstain may only become clear after interacting with the environment. We study this problem across web shopping, terminal environments, and question answering, evaluating 13 LLM-as-
agent systems and 2
agent scaffolds on more than 28,000 task instructions. Our results show that the main challenge is not only whether
agents can abstain, but also when they abstain. Some
agents never abstain when they should, while others do so only after many unnecessary interactions. This gap is especially large on tasks where the instruction appears feasible until the environment reveals otherwise (e.g., no valid result matches the instruction). We further find that model scale,
reasoning, and
agent scaffolding affect abstention in different ways, where larger or more capable models sometimes perform worse at timely abstention. Finally, we introduce CONVOLVE, a context engineering method for improving agentic abstention that distills interaction trajectories into reusable stopping rules. On held-out WebShop tasks, CONVOLVE substantially improves timely abstention without updating model parameters, raising Llama-3.3-70B's timely recall rate from 26.7 to 57.4. Our
dataset and code are available at https://agentic-abstention.github.io.
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Artificial Intelligence · Cross-listed from cs.CV
CCRC: A Change-Aware Captioning and Reasoning Chain for Image Change Captioning and Segmentation
Jinhong Hu, Xiaoping Wang, Shuyin Huang et al. (+3 more)
Understanding and localizing subtle changes between paired images is critical for tasks such as surveillance and image editing. However, traditional Image Change Captioning (ICC) methods lack spatial grounding, limiting their precision. We introduce Image Change Captioning and
Segmentation (ICCS), a new
multimodal task that jointly requires structured change description and pixel-level localization. To address ICCS, we propose the Change-aware Captioning and
Reasoning Chain (CCRC), a dual-chain framework that decouples semantic
reasoning from spatial
segmentation. The first chain, Chain-of-Change-Captioning (CCC), enhances fine-grained change perception via a visual fusion module based on Multi-Head Change-aware
Attention inserted between the visual and
language components of a
Multimodal Large
Language Model (MLLM). CCC also determines whether a change is segmentable. If not, it alone generates the caption. Otherwise, the second chain, Chain-of-Change-Segmenting (CCS), is activated, leveraging spatial priors from CCC and refining masks with a Change-aware
Token Refiner for accurate boundary localization. We evaluate CCRC on both synthetic and real-world change detection
benchmarks with pixel-level supervision. Experiments show CCRC achieves state-of-the-art performance.
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All authors Jinhong Hu, Xiaoping Wang, Shuyin Huang, Guojin Zhong, Kaitai Liu, Kai Lu
Artificial Intelligence
ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models
Guanglong Sun, Shuang Cui, Bo Lei et al. (+6 more)
Test-time adaptation (TTA) of
vision-
language models (VLMs) is essential for their robust deployment in dynamic, real-world environments. However, existing TTA methods often adapt locally without accumulating knowledge over time, or operating within a single modality without exploiting VLMs' inherently multi-modal nature. Inspired by the Complementary
Memory systems of the biological
brain, we propose ComMem, an innovative approach that mimics the distinct but cooperative roles of the hippocampus and neocortex to enable effective TTA for VLMs. ComMem consists of two key components: a fast-adapting detailed
memory, akin to the hippocampus, that forms a dynamic visual cache from high-confidence test samples; and a slow-integrating abstract
memory, akin to the neocortex, that continually refines global textual prototypes. For each test instance, ComMem jointly optimizes both
memory systems to ensure cross-modal consistency. Extensive experiments on 15
benchmark datasets show that ComMem significantly outperforms state-of-the-art methods under both natural distribution shifts and cross-
dataset generalization, offering a promising direction for enhancing VLMs' practical adaptability.
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All authors Guanglong Sun, Shuang Cui, Bo Lei, Liyuan Wang, Zihan Zhai, Hongwei Yan, Hang Su, Jun Zhu, Yi Zhong
Artificial Intelligence
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Liang Zhang, Gaojie Jin, Yao Shi et al. (+4 more)
The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems.
Adversarial attacks on trajectory prediction can significantly impair the accuracy of predicted trajectories, leading to hazardous driving behaviors. While heuristic defense strategies have been implemented to enhance the robustness of trajectory prediction models, these measures often fail against more sophisticated, targeted
adversarial attacks. Hence, there is a pressing need to establish verifiable safety assurances for trajectory prediction models. In this paper, we extend the traditional Randomized Smoothing framework to "TrajRS", which provides a certified robust radius for smoothed trajectory predictors. We clarify and expand the formal definitions of robustness in trajectory prediction and tailor the practical TrajRS scheme specifically to "robustness for the optimal prediction" and "robustness for all possible predictions". An extensive set of experiments demonstrates that TrajRS effectively achieves robustness certification for all smoothed pedestrian trajectory predictors in this work.
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All authors Liang Zhang, Gaojie Jin, Yao Shi, Quanzhi Li, Cheng-Chao Huang, David N. Jansen, Lijun Zhang
Artificial Intelligence · Cross-listed from cs.CL
SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
My Chiffon Nguyen, Aulia Adila, Saksorn Ruangtanusak et al. (+4 more)
While AI development and evaluation for Southeast Asia (SEA) has grown rapidly,
agent capabilities in regional
languages are still poorly understood despite its importance to sovereign AI. To fill this gap, we introduce SEATauBench, the first
agent-focused evaluation framework for SEA sovereign AI. It adapts Tau2-Bench to five
languages—Mandarin, Vietnamese, Thai, Indonesian, and Filipino—and evaluates
agents across progressively localized settings that vary the
language of user-
agent interaction, tool specifications, and task domains. Across three models, we find that English
agent capabilities transfer reasonably well when only the conversation
language changes, but quality and robustness degrade sharply as more task contexts are localized, with the largest losses in full domain adaptation. We also highlight the limits of English-only
agent assessment for predicting
agent capabilities in SEA
languages. More broadly, SEATauBench provides a diagnostic
benchmark and reusable adaptation pipeline for building reliable
multilingual agents for linguistically diverse regions. Data and code can be accessed at github.com/SEACrowd/SEATauBench
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All authors My Chiffon Nguyen, Aulia Adila, Saksorn Ruangtanusak, Kittiphat Leesombatwathana, Vissuta Gunawan Lim, Patomporn Payoungkhamdee, Samuel Cahyawijaya
Artificial Intelligence
The Two Genie Game: Adoption and Welfare in Audit-Grounded AI Governance
Darrell Lewis-Sandy
We ask under what conditions an
agent with a harm-minimizing policy can displace an approval-seeking (RLHF)
agent in a competitive
market, and when that policy is sufficient to prevent community harm. We use evolutionary game theory (finite-population Moran-
Fermi pairwise comparison) to formalize this subject to assumptions of wisher hindsight, peer testimony, a monotone harm ledger, sufficient information density of community feedback, and a finite, depleting resource pool, in a negative-sum environment. We show that adoption is favored when the prior distributions on how readily wishers attune to community sentiment are monotone, exhibit endpoint inversion, and have a centro-symmetric pairing property, and demonstrate this with several long-tailed priors (Hill, Pareto, Lomax, Frechet). Where it is favored, a critical adoption level separates communities that drift back to the approval-seeking
agent from those for which the audited
agent fixes; above that level fixation is the overwhelmingly likely outcome. We derive when fixation is attainable as a bound on the effective (informational) size N_c of the community, which must be small enough to allow fixation before depletion. We present these as
Theorems 5.4 and 5.5; the algebraic and finite-grid backbone is machine-checked in Lean 4, with the barrier-crossing asymptotics retained as explicit hypotheses. We show that a self-audited
agent with a community ledger is not, in general, sufficient to prevent community harm. Sufficiency depends both upon the
alignment of the
agent's audit with community values and the timeframe over which harm is evaluated. Regardless of
alignment, once adoption reaches dominance, the state is absorbing. The same policy that reduced harm under
alignment becomes a trap, welfare-negative under misalignment and, even under
alignment, one that locks in harm deferred past the adoption horizon.
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Artificial Intelligence
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
Yupeng Chang, Yuan Wu and Yi Chang
Critic-free reinforcement learning with verifiable rewards (RLVR), exemplified by Group Relative Policy Optimization (GRPO), avoids training a value function (critic) and reduces
memory and compute overhead relative to critic-based PPO pipelines for aligning large
language models. However, GRPO-style advantage estimation depends on
prompt-local (within-
prompt-group) reward statistics and can be unstable. In particular, when all rollouts in a
prompt group receive identical rewards, the within-group reward variance becomes zero, and group normalization yields zero advantages for that group, impeding learning in cold-start regimes with binary verifiers. We introduce BV-Blend, a critic-free framework that stabilizes advantage estimation by combining
prompt-local on-policy statistics with semantic-cluster-conditioned historical moments. BV-Blend maintains EMA-tracked reward moments for each cluster, derives a confidence weight from a standard error of the mean (SEM) proxy, and uses this weight to blend historical and
prompt-local baseline and variance statistics into a standardized advantage for PPO-style clipped updates. Experiments on verifiable
reasoning benchmarks show that BV-Blend improves training stability and performance, and remains robust in regimes where group-normalized methods may stall.
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Artificial Intelligence
COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models
Ziqi Zhou, Weize Quan, Mining Tan et al. (+6 more)
Composition is a high-level visual intent that governs where subjects are placed and how a scene is organized, yet current unified
multimodal models remain unreliable at fine-grained composition recognition and struggle to turn such intent into controllable generation. We present COMPASS, the first unified
multimodal framework that grounds composition-intent control in a single system spanning both composition perception and composition-guided generation, with a shared expert
token $\tau_c$ as the central intent anchor. On the perception side, COMPASS injects composition expertise into an MoE backbone in a minimally invasive manner and distills the inferred intent into $\tau_c$. On the generation side, COMPASS reuses $\tau_c$ as a global conditioning signal that steers the denoising trajectory, effectively converting passive composition analysis into explicit layout control. To support systematic instruction-following composition learning and evaluation at scale, we construct Comp-11, a large-scale
dataset with an 11-class taxonomy and
reasoning-augmented annotations. Extensive experiments show that COMPASS substantially improves category-level composition understanding and delivers more composition-consistent,
prompt-faithful generation than strong baselines.
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All authors Ziqi Zhou, Weize Quan, Mining Tan, Zhihan Chen, Dandan Zheng, Jingdong Chen, Jun Zhou, Weiming Dong, Dong-Ming Yan
Artificial Intelligence
An AI agent for treatment reasoning over a biomedical tool universe
Shanghua Gao, Ayush Noori, Richard Zhu et al. (+13 more)
Treatment
reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy. It is inherently iterative: candidates are weighed against many constraints, revised as evidence emerges, and grounded in verifiable sources. Here we introduce ATHENA-R1, an AI
agent for treatment
reasoning across all FDA approved drugs since 1939, trained by reinforcement learning over a universe of 212 biomedical tools. At each step it identifies missing information, selects and runs relevant tools, and incorporates the evidence. To train it without human-annotated traces, we build a two-level self-learning framework: multi-
agent systems construct the tools, tasks, and
reasoning trajectories for supervised fine-tuning, then reinforcement learning with scientific feedback rewards
reasoning quality (evidence gathering, grounded tool use, logical non-redundancy). Across five
benchmarks of 3,168 drug
reasoning tasks and 456 patient treatment cases, ATHENA-R1 outperforms
language models and tool-use systems, reaching 94.7% accuracy on open-ended drug
reasoning and 82.9% on treatment
reasoning, 17.8 and 10.7 points above GPT-5. In blinded evaluations by experts from 28 rare disease organizations, it is preferred over reference models on all criteria, and physicians rated it favorably on complex hospitalized cardiovascular and infectious-disease cases. Adverse-event hypotheses it generated, tested in electronic health records from 5.4 million patients, reached adjusted odds ratios of 1.48-1.84, with no elevation among negative controls. Because it requires knowing what evidence to seek before concluding, treatment
reasoning has long been hard for AI; we show it can be reframed as a learnable process of iterative evidence gathering that reinforcement learning can train AI to perform.
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All authors Shanghua Gao, Ayush Noori, Richard Zhu, Curtis Ginder, Zhenglun Kong, Xiaorui Su, Justin Kauffman, Benjamin S. Glicksberg, Joshua Lampert, Ankit Sakhuja, Ashwin Sawant, ATHENA-R1 Evaluation Consortium, David A. Clifton, Noa Dagan, Ran Balicer, Marinka Zitnik