Artificial Intelligence
Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models
Shaopeng Wei, Yufei Cheng, Wenxi Sun et al. (+3 more)
The rapid development of large
language models (LLMs) has renewed interest in
agent-based modeling (ABM). However, current LLM-based ABM research faces several key challenges: modeling evolving
agent-environment interactions, enabling flexible counterfactual
reasoning, and automating simulation workflows for scientific research. In this paper, we propose Eco3S, a socio-economic system simulation framework for economic research and policy analysis that addresses these challenges through three key mechanisms: (1) Co-evolving Environment Design, a bidirectional feedback loop where
agents and the environment co-evolve, producing realistic emergent behaviors; (2) Structural Causal Simulation, a structural causal model (SCM)-inspired counterfactual mechanism that allows flexible interventions for diverse causal inference tasks; (3) Simulation-Analysis-Refinement Paradigm, a self-corrective mechanism that iteratively refines experimental designs based on prior simulation results. Experiments on diverse economic scenarios confirm Eco3S's effectiveness in replicating multiple established economic studies (canal decay, origins of governance, and information propagation) and phenomena across domains. Additional results further demonstrate its scalability and generalizability, highlighting the framework's potential for rigorous economic research and policy-making.
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All authors Shaopeng Wei, Yufei Cheng, Wenxi Sun, Yepeng Ding, Yu Zhao, Gang Kou
Artificial Intelligence · Cross-listed from cs.MA
The Implementation Lottery: Auditing Idea Reliability in Automated Research
Jingjie Ning, Shanshan Zhong, Xiaochuan Li et al. (+2 more)
Automated research
agents use program scores to judge ideas. We call variation in this evidence across implementations the implementation lottery. We introduce an Idea Reliability Audit that freezes mechanism specifications, samples independent programs, and compares selected code with fresh implementations of its mechanism. Across 3,048 assignments on 31 tabular classification tasks, all four primary aggregation tests have Holm-adjusted $p\geq0.56$. Under mean-of-five selection, the prespecified secondary intention-to-treat comparison gives selected-code premiums of 0.38 [0.08, 0.82] and 0.45 [0.05, 1.12] accuracy-equivalent points for Bounded and Agentic execution, respectively. Minimum task-deletion means are 0.20 and 0.14. Fidelity conditioning exposes concentration: the Agentic premium falls from 0.33 to 0.01 when one task is removed. Post-outcome analysis finds cross-split variation on 41 of 70 paired cards per process. Exploratory replay gives nearly equal Bounded point losses at four and twenty implementations; Agentic point losses decrease across the four evaluated budget rules. Under duration costs, one seed per program minimizes fitted common-design variance. The two-seed design becomes preferable when implementation-to-seed cost ratios exceed approximately 13 or 9.5 in the continuous-budget model. The audit distinguishes evidence for reusing a selected artifact from evidence for implementing its idea again.
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All authors Jingjie Ning, Shanshan Zhong, Xiaochuan Li, Ji Zeng, Chenyan Xiong
Artificial Intelligence · Cross-listed from cs.CV
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
Nand Lal Yadav, Rajesh Kumar, Satyendra Singh et al. (+1 more)
With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. Convolutional neural networks have given promising results when used for diagnosing diseases using imaging tests. In this study, we investigate the potential of applying deep learning
algorithms such as VGG16, VGG19, and ResNet50 for classification of lung ailments based on X-ray images. A detailed analysis of the aforementioned models' performances was conducted to assess how well they can classify various types of lung ailments, including pneumonia, tuberculosis, lung cancer, and normal lungs. In order to do that, these deep learning models were trained on a vast amount of X-ray images. The results of our study show that while all three models provide good results, ResNet-50 performs best in comparison with other models due to its efficiency and high level of accuracy. We believe that these deep learning models can be successfully implemented in the practice of diagnosing pulmonary diseases in the future. It helps with early disease detection and improves patient outcomes.
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All authors Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh
Artificial Intelligence · Cross-listed from cs.CR
Blind, Not Weak: A Best-of-Suite Safety-Utility Frontier for Recover-and-Reguard Defenses Against Encoded VLM Jailbreaks
Haoyu Zhang, Zhuoxi Wang, Shibo Zheng et al. (+9 more)
Safety classifiers ("guards") are the dominant black-box defense for
vision-
language models, yet a guard judges an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a classical
language, code, or text rendered inside an image slips past a guard that would block it in plain
language - the decode gap. The standard fix is a preprocessor that recovers image content and decodes the encoding before the guard. We build one and evaluate it against an ensemble of eleven encoding attacks - six published implementations, one standard encoding baseline, one adapted and three author-constructed renders - counting a behavior as broken if any attack succeeds. Restoring a view the guard never had is what buys coverage - block rates on image renders go from exactly zero to 67-90% - and what it costs in benign traffic is set by the guard, not by the mechanism: one guard pays 9 benign blocking points for the same 70-point gain another pays 69 for. It still does not make the system safer: against an attacker free to choose among eleven encodings, closing one channel relocates the success rather than removing it, and no ensemble contrast for that step survives multiple-comparison correction. What does lower ensemble attack success is a reguard step that re-screens the recovered pre-decode surface, and it is the one every guard pays for: it raises benign over-refusal on all ten guard-target pairs, where restoring a single channel raises it on some and not others. Across the full guard x target x condition factorial, no configuration reaches an ensemble attack-success rate at or below 40% while holding benign over-refusal under 70%. That empty region is a property of the configurations we sample, not a bound on what recovery-based defenses can reach, and we breach its safety half ourselves.
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All authors Haoyu Zhang, Zhuoxi Wang, Shibo Zheng, Hanwen Liu, Yi Feng, Haowen Xu, Xiangchen Guan, Yang Chen, Zijian Xiao, Xiao Luo, Mohammad Zandsalimy, Shanu Sushmita
Artificial Intelligence · Cross-listed from cs.DC
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
Xiaoxiao Jiang, Suyi Li, Sheng Yao et al. (+5 more)
Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently. Existing platforms typically deploy each workflow as an opaque GPU function, provisioning, placing, and scaling all constituent models in the workflow together. This monolithic design obscures workflow structure, inflates scaling overhead, forces users to manage low-level GPU coordination, and limits fine-grained
fairness in multi-tenant clusters. In this paper, we present ServerlessT2I, a serverless-native system that decomposes a T2I workflow into loosely coupled model functions that can be independently managed and scheduled. By explicitly managing individual model execution, ServerlessT2I enables per-model scaling, declarative workflow composition, transparent GPU-resident communication, and
fairness-aware scheduling. To make this decomposition efficient, ServerlessT2I harvests slack GPU
memory left idle by compute-bound T2I inference to build a data plane that reduces model loading and data communication overheads. \sys{} further introduces a fair scheduler for multi-tenant serving. Using production traces, ServerlessT2I sustains up to 2$\times$ higher request rates than existing T2I workflow serving systems with the same GPU budget; for a fixed request rate, it saves up to 3$\times$ GPU resources while satisfying service level objectives (SLOs).
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All authors Xiaoxiao Jiang, Suyi Li, Sheng Yao, Tianyu Feng, Lingyun Yang, Dapeng Nie, Haoran Yang, Wei Wang
Artificial Intelligence · Cross-listed from cs.MA
A Persona-based Rate Action Index
Hayden Helm and Andrew Dassori
We propose an index for predicting the U.S.\ Federal Open
Market Committee (FOMC) decision to hike/hold/cut the current federal funds target rate based on how a collection of personas responds to current
market conditions. To construct the index, we collected a new
dataset consisting of nearly $25{,}000$ retrievable chunks from publicly available data. We partition the data into per-member corpora and use each as the
retrieval database of a generative system we refer to throughout as a “persona”. We first evaluate the personas across two complementary components of likeness: identifiability and detectability. Each persona's behavior is highly attributable (average member-conditional recall is $ 8\times $ chance) and generated content is nearly indistinguishable from held-out real content ($\hat\tau_{\mathrm{det}} = 0.23$ against a $0.15$ floor). We then present evidence that query-conditioned representations of the personas capture members' monetary-policy stance relative to a known hawk–dove reputational ordering (Kendall's $\tau = 0.63$, $p < 0.001$), substantially outperforming
retrieval-only representations. These representations vary with time and current
market conditions and form the basis of our proposed persona-based rate action index. For the $2022$–$2025$ period the index tracks the rate cycle (Kendall's $\tau = 0.68$, $p < 10^{-6}$) and can be used to construct a simple classifier that predicts per-meeting outcomes at non-trivial accuracy ($0.69$ versus a $0.47$ base rate). Importantly, the index outperforms informative baselines and leads the federal funds target rate by roughly three quarters. As far as we are aware, our results are the first to demonstrate the ability to capture time-varying group behavior via a collection of digital personas.
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Artificial Intelligence · Cross-listed from cs.LG
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
Jingbo Cui, Jitao Zhao, Di Jin et al. (+1 more)
Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research. However, local structural patterns may vary across graphs and even among nodes within the same graph. Despite such structural variation, most existing GFMs rely on manually designed propagation schemes and apply them to new graphs largely unchanged. Such fixed schemes may not suit the diverse structural patterns of different nodes. This raises a key question: can each node autonomously determine how information should be propagated through the graph? We refer to this capability as information-flow control. Inspired by recent advances in
agent technology, we formulate this problem as
agent-based decision making and treat each node as an
agent. Accordingly, we propose AgentGFM, in which all node
agents follow a shared end-to-end trainable policy rather than using independent models. For adaptive information-flow control, each node interacts with the graph through a predict-act-observe-correct process. During the act stage, the node makes three decisions: source reception, signal-channel selection and gain-aware node-wise halting. The resulting observation is compared with the prediction and their discrepancy is used to correct the node state and guide subsequent interactions. Extensive experiments across node-level, graph-level and large-scale transfer scenarios demonstrate the effectiveness of AgentGFM across diverse graph
topologies.
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All authors Jingbo Cui, Jitao Zhao, Di Jin, Dongxiao He
Artificial Intelligence · Cross-listed from cs.NE
Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
Manuel Rodriguez
Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is limiting in process systems, where state variables are often coupled through shared physical parameters. Independent symbolic regression can give accurate individual equations that are difficult to interpret as one model. We present a neuro-evolutionary symbolic regression method for coupled multi-output systems. The method searches for a shared symbolic backbone: a set of latent symbolic units that is discovered once and reused by several outputs through sparse additive or multiplicative read-outs. The discrete model structure is evolved by mutation and crossover, whereas the continuous parameters are tuned by gradient descent and inherited by the offspring. The method is assessed on a set of
benchmarks with known ground truth and on a hydrothermal liquefaction yield case. The results show that coupling is not a general route to lower prediction error. Its main contribution is the enforcement and diagnosis of cross-output consistency when a physically shared factor is embedded in a latent expression and is weakly identifiable from the data. This occurs for Langmuir-Hinshelwood and site-coverage denominators, for which independent PySR does not close the consistency gap or recover the same shared form. Conversely, when each output is already identifiable, as in the Van de Vusse
benchmark, independent symbolic regression matches or improves the coupled model. The proposed framework, rather than a general purpose predictor, is a structured shared-mechanism extractor. Its value is highest when the target structure is sparse, shared, weakly identifiable or constrained by closure.
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Artificial Intelligence · Cross-listed from cs.LG
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
Ashmith Atmuri and Yashaswini Rao Bhogarajula
We introduce CMP (Cognitive
Memory Primitive), a continual-learning architecture that repre?sents inputs as sparse relational codes, stores them in a two-tier competitive
memory, and learns through local updates without end-to-end backpropagation through its feature-generating system. We investigate whether combining sparse representations, local learning, and persistent
memory can reduce catastrophic forgetting relative to conventional backpropagation-based continual?learning approaches. On a controlled domain-incremental byte-level
language modeling protocol, CMP demonstrates substantially lower backward transfer than a parameter-matched Trans?former trained with online Elastic Weight Consolidation (EWC). Across a three-seed replicated 15-domain experiment, CMP exhibits stable forgetting behavior, while separate head-to-head comparisons and domain-order analyses show consistently lower forgetting than the evaluated
Transformer baseline under the reported experimental settings. We report these findings alongside a substantial single-domain accuracy gap relative to the
Transformer, a null result on a
vision benchmark, and a documented failure to combine CMP with an independent accuracy-improving mechanism, reflecting our commitment to reporting both positive and negative outcomes. These results suggest that the combination of sparse representations, local learning, and persistent
memory is a promising direction for continual learning, while motivating further investigation into the respective roles of learning rules, representations, and architectural design in mitigating catastrophic forgetting.
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Artificial Intelligence · Cross-listed from cs.DB
A Graph-Native Bitemporal Memory Store for Conversational AI Agents
Alp Niksarli and Gopesh Baheti
Conversational AI
agents commonly lack persistent
memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party
memory service, either exhaust the model's context budget or send personal data through infrastructure the user does not control. We describe a
memory store that avoids both problems: an
agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model. Each
memory is stored as an immutable identity node linked to versioned content nodes carrying two closed-open time intervals: valid time (when the fact was true in the world) and transaction time (when the database recorded it). This design supports point-in-time semantic
retrieval without physically overwriting history. Semantic edges between related
memories are maintained automatically at write time using cosine similarity over 1024-dimensional
embeddings. We evaluate the system on LongMemEval, a 500-question
benchmark spanning six question types designed to stress long-term
memory. Across 60 sampled questions, the current-state semantic search path achieves 46.7% R@10 overall, rising to 80% on knowledge-update questions. The time-travel path yields 80% R@10 on knowledge-update but decreases recall on temporal-
reasoning questions (50% to 37.5%), a consequence of post-filter dilution that points directly to a concrete design improvement. We discuss what these results reveal about the limits of pure
retrieval for different question types and what each failure mode suggests for future work.
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Artificial Intelligence · Cross-listed from cs.LG
HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
Hei Yi Mak, Shadan Golestan, Hoang Le et al. (+10 more)
We present, to our knowledge, the first end-to-end FP4 RL post-training, in which both the rollout and training policies, including their forward and backward passes, operate at 4-bit precision. A systematic study reveals that the dominant source of degradation in FP4 RL is not training-side quantization error but rollout activation quantization: outliers stretch the dynamic range so far that a large number of activation values underflow to zero under FP4. Counterintuitively, restoring the training policy to higher precision while keeping the rollout in FP4 makes accuracy worse than full FP4 baseline, exposing rollout-training mismatch as the principal failure mode and ruling out standard pretraining-style fixes. We address this with Rollout Residual Quantization (Rollout-ResQ): a single residual correction term constrained to a hardware-friendly sparsity pattern, added only to the FP4 rollout matmul – a lightweight correction that recovers most of the precision lost to outlier-driven underflow without inflating the rollout's compute footprint. On Qwen2.5-3B and Qwen2.5-Math-7B, Rollout-ResQ paired with the HiFloat4 (HiF4) format – whose three-level hierarchical scaling preserves resolution under FP4's tight 4-bit budget – closes the accuracy gap to BF16 from 4.9% to 1.1%, bringing fully quantized FP4 RL within striking distance of full precision. Applied to the open-standard MXFP4, the same recipe narrows the gap from 13.6% to 5.3%, revealing that FP4 format choice is a key factor that determines the ceiling on recoverable accuracy. Together, these results establish HiF4 as the enabling format for end-to-end FP4 RL post-training, and Rollout-ResQ as the activation-side mechanism that makes the gap to BF16 closable.
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All authors Hei Yi Mak, Shadan Golestan, Hoang Le, Mehran Taghian Jazi, Yunke Peng, Yaoyuan Wang, Yao Wang, Junsong Wang, Tianchi Hu, Fengchen He, Guipeng Hu, Tanzila Rahman, Anandharaju Durai Raju
Artificial Intelligence
Evidence-Ledger Adjudication for Claim-Evidence Traceability
Gengyu Chen, Yongjie Yu and Weiling Wang
AI
agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them. We study evidence-ledger adjudication: a claim-evidence traceability workflow that pairs each claim with an evidence packet, assigns a support relation, and routes unsupported, contradicted, or mixed-evidence claims back to the author. The empirical core is a 2,335-row blind
benchmark built from independent external labels in AVeriTeC,
CLIMATE-FEVER, and SciFact. Gold relations and source evidence labels are hidden during prediction and joined only for scoring. On this
benchmark, the
agent evidence-ledger condition achieves 0.676 relation accuracy and 0.601 macro-F1, compared with 0.383 accuracy and 0.303 macro-F1 for the best non-
agent baseline. It also routes 1270/1435 claims whose gold labels indicate contradiction, missing evidence, or mixed evidence, while routing 295/900 supported claims. These results show that evidence-ledger adjudication can turn heterogeneous evidence packets into an auditable traceability layer for AI-assisted writing.
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Artificial Intelligence · Cross-listed from cs.LG
Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning
Gong Gao, Xiao Lai, Ziqi Xie et al. (+3 more)
Deep off-policy reinforcement learning
algorithms for continuous control typically rely on neural value function approximation to guide policy improvement. However, temporal-difference (TD) learning introduces noisy targets, resulting in non-stationary optimization, while greedy policy updates amplify early-stage estimation errors. The recursive propagation of such errors leads to persistent overestimation bias and degraded training stability in actor-critic methods. Existing approaches attempt to alleviate this issue via prioritized sampling or modified value learning objectives, but often overemphasize high-uncertainty transitions caused by limited data coverage or bootstrapping errors, thereby further amplifying bias.In this paper, we propose Collaborative Weighting Actor-Critic (CWAC), a unified framework that explicitly accounts for predictive uncertainty in value estimation. CWAC employs distributional critic to model return uncertainty and introduces a collaborative weighting mechanism that jointly reweights TD-errors and uncertainty, enabling robust learning from reliable samples while suppressing noisy updates. In addition, we incorporate a
stochastic pessimistic value estimation scheme via sampling from the return distribution, which effectively mitigates error propagation during policy improvement. CWAC can be seamlessly integrated into existing off-policy
algorithm frameworks such as SAC, TD3, and DDPG with minimal overhead. Empirical results demonstrate that our proposed method significantly enhances performance across a diverse range of simulated tasks. Our code is publicly available at https://anonymous.4open.science/r/CWAC-348E.
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All authors Gong Gao, Xiao Lai, Ziqi Xie, Guojie Chen, Xianhui Liu, Weidong Zhao
Artificial Intelligence · Cross-listed from cs.AR
LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving
Ming-Yen Lee, Hanchen Yang, Faaiq Waqar et al. (+4 more)
The energy consumption of Large
Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and thermal constraints and rising electricity costs. A key contributor to chip energy dissipation is data movement between limited on-chip cache and off-chip High Bandwidth
Memory (HBM). Meanwhile, emerging
memory technologies such as monolithic 3D (M3D) integration of cache
memories at the Back-End-Of-Line (BEOL) of logic chips enable larger and denser on-chip
memories, creating new opportunities to reduce costly off-chip traffic. However, it remains unclear whether continuously scaling on-chip
memory using emerging technologies can effectively improve the energy efficiency of LLM serving. To address this gap, we develop LLMET (LLM with Emerging Technology), a validated cross-layer simulation framework, and conduct a comprehensive study on the impact of large-capacity on-chip
memory technologies across a broad range of models, applications and platforms. Utilizing M3D technology to expand the L2 cache from 40MB to 1GB yields a 44% reduction in chip energy during the Llama3.1-70B prefill phase with a 16K context window, based on LLMET simulation on a dual NVIDIA A100 GPU setup. On the 8x NVIDIA B200-like platform, extending the L2 cache from 128MB to 4GB saves the prefill energy by up to 24%. For the edge platform and workloads, the decode energy saving reaches 30% when increasing the 8MB cache size to 256MB. These results highlight the promise of ultra-large on-chip
memories for energy-efficient LLM serving systems.
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All authors Ming-Yen Lee, Hanchen Yang, Faaiq Waqar, Harsono Simka, Tushar Krishna, Muhammed Ahosan Ul Karim, Shimeng Yu
Artificial Intelligence
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
Peng Yin, Kai Li, Yifan Zhang et al. (+1 more)
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics. While Large
Language Models (LLMs) offer a promising avenue for automated design, unconstrained code generation often yields mathematically invalid or numerically unstable solutions under strict scientific computing constraints. To bridge this gap, we propose EvoPINN, an agentic framework that reformulates PINN development from labor-intensive manual design into a rigorous, execution-grounded
algorithm discovery problem. EvoPINN navigates a modular search space by decoupling neural representations from training programs, utilizing an LLM
agent to iteratively propose
memory-conditioned programmatic modifications. To ensure scientific validity, all candidates undergo strict structural verification and budget-matched PDE evaluation. Extensive experiments across diverse PDE regimes (oscillatory,
elliptic, dissipative, and nonlinear transport) demonstrate that EvoPINN discovers PDE-specialized learning
algorithms that significantly reduce relative $L_{2}$ error compared to baselines. Crucially, EvoPINN autonomously invented SLRC-PINN, a novel architecture whose performance gains persist under rigorous parameter-matched comparisons, establishing the viability of execution-grounded
agents for discovering genuinely new scientific computing mechanisms.
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All authors Peng Yin, Kai Li, Yifan Zhang, Jian Cheng
Artificial Intelligence · Cross-listed from cs.SD
Audio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection
Haotian Mo, Jie Liu, Siqi Shen et al. (+8 more)
Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a
Diffusion Transformer (DiT), trained only on bona fide
speech, as a frozen reconstruction probe. Reconstructions at masking ratios 0.5, 0.75, and 0.9 yield explicit multi-ratio residual maps. Because these residuals are domain sensitive, our audio-anchored detector passes the projected frozen-WavLM auditory representation into the fusion sum without gate-based attenuation and uses residuals only as a scalar-gated additive correction. The pre-specified seed-42 run obtains 6.5442% EER / 0.18456 min-DCF on ASVspoof 5 Eval and 13.8372% / 0.36921 on ITW Full; three-seed means are 6.8885 (0.3308)% and 15.3328 (2.0719)%. The latter is below a separately optimized WavLM-ResNet18 reference under both supervision settings. Auxiliary supervision raises dynamic competitive fusion from 18.4007% to 25.2968% mean ITW EER, worsening all three seeds. The results support reconstruction residuals as complementary evidence and motivate a non-competitive auditory path for ASVspoof 5-to-ITW transfer, without claiming a componentwise causal ablation of anchoring alone.
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All authors Haotian Mo, Jie Liu, Siqi Shen, Songzhu Mei, Xinhai Chen, Xiangyang Wang, Yigui Feng, Shuai Li, Gencheng Liu, Keqi Yang, Qinglin Wang
Artificial Intelligence
Kawai Chung, Chunkit Chan, Yauwai Yim et al. (+12 more)
Multimodal Large
Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation
reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MultivationBench, a
benchmark designed to rigorously evaluate
multimodal motivation
reasoning within story-driven visual narratives. The
benchmark builds upon established psychological frameworks - Maslow's hierarchy and Reiss's basic desires - and requires models to integrate accumulated
multimodal context to infer evolving motivations. Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation
reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic
reasoning essential for human-like social understanding.
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All authors Kawai Chung, Chunkit Chan, Yauwai Yim, Yuxuan Liu, Haochen Shi, Weiqi Wang, Qing Zong, Tianshi Zheng, Yixuan Fu, Kai Chung Wong, Hao Liang, Yifan Gao, Xi Yang, Janet Hui-wen Hsiao, Yangqiu Song
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
Zekun Ren, Hongzhao Tan, Jiaen Yee et al. (+1 more)
Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (GUI) orchestration layer, PUDA creates a command-line runtime environment that lets
agents observe, orient, decide, and act over experiments while hardware execution remains deterministic, atomic, and auditable. Headless by design, devices appear through discoverable command-line interfaces, JSON protocols are routed through a distributed messaging system, and command responses, data products, and reports are preserved as structured records. PUDA organizes protocols, runs, samples, measurements, and command logs into an AI-native data structure linked by run identifiers and timestamps, preserving provenance from submitted protocol through hardware response to resulting data products. PUDA separates scientific orchestration from physical operation and data telemetry:
agents choose experiments, while PUDA executes validated commands and captures provenance-linked state, responses, and data. The contribution is not another optimizer, orchestrator, or recipe
language. It is a practical execution and data environment for agentic SDLs; the broader physical AI implication is that PUDA provides an AI-native hardware harness for AI systems to interact with physical tools.
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All authors Zekun Ren, Hongzhao Tan, Jiaen Yee, Kedar Hippalgaonkar
Artificial Intelligence · Cross-listed from cs.CL
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
Ruxi Gu, Zhenliang Zhang, Zilei Wang et al. (+1 more)
Large
language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and
reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step
reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification. In this work, we propose ForgetBench, a
benchmark for measuring forgetting dynamics in the long-term parametric
memory of LLMs under continual knowledge editing. ForgetBench introduces two complementary evaluation paradigms, namely concept-based QA and scenario-based QA, to probe the retention of isolated facts and structured relational knowledge. Building upon a sequential editing framework, we construct temporally ordered knowledge streams and evaluate model behavior across multiple editing stages. To quantify these dynamics, we introduce a unified evaluation framework that indexes
memory age by the number of subsequent edits and measures temporal decay, retention strength, and cross-instance stability. Extensive experiments across diverse models and editing methods reveal distinct forgetting trajectories and a recurring tension between long-term retention and generalization quality. Our findings highlight the need for more robust
memory mechanisms that can effectively acquire, update, and preserve knowledge over time in future LLMs.
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All authors Ruxi Gu, Zhenliang Zhang, Zilei Wang, Wei Wang
Artificial Intelligence
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
Yiming Cai, Fangjie Yu, Meiqing Yu et al. (+3 more)
World models must learn the joint dynamics of states, actions, events, and observations, yet existing
video, robotics, and simulation
datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state
dataset and protocol derived from industrial computer graphics production pipelines. CG-World explicitly records intermediate states, including
multimodal semantics, spatial structure, skeletal and controller states, motion curves, camera and lighting parameters, physics caches, contact events, and multi-pass renderings. CG-World v1 contains approximately 850,000 temporally aligned segments of 1-5 seconds. It separates latent states, observations, relations, events, and branch metadata, and organizes them into unified spatiotemporal samples. To support intervention learning and counterfactual
reasoning, CG-World defines a branch lineage covering factual trajectories, observation interventions, action interventions, mechanism interventions, and strict counterfactual branches, with intervention targets, invariants, and alternative outcomes explicitly recorded. We evaluate the
dataset on geometry-conditioned
video generation, action prediction, and closed-loop
vision-
language-action policy transfer. Results show that CG-World provides reusable structured supervision for controlled generation, action modeling, and
embodied policy transfer. We plan to expand CG-World through continued data collection and community collaboration toward a shared data infrastructure for world models, Physical AI, and
embodied intelligence.
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All authors Yiming Cai, Fangjie Yu, Meiqing Yu, Ziyue Shi, Pengfei Yuan, Yong Guo
Artificial Intelligence · Cross-listed from cs.CL
Mergeable Model-Side Aggregation States for Long-Context Language Models
Dachuan Song, Junyu Yin, Zechen Hu et al. (+1 more)
A known limitation of long-context
language models is their increasingly unreliable performance in non-additive, set-based aggregation as context length grows. Examples include cardinality estimation, set relationships, and grouped statistics, which widely exist in logs, program outputs, tables, and multi-turn conversations. To provide the aggregation state required by these tasks, we introduce a model-side aggregation interface that maintains compact Hash-based HyperLogLog (HLL) sketch states alongside a frozen
language model. While the model processes the context, an extractor maps each relevant record to a canonical identity. The identity is then hashed and updates the HLL state. These states can be merged across context segments and/or read out directly for downstream
reasoning, avoiding an additional generate-execute-return cycle. We validate the proposed approach by setting the HLL state size as 2 KiB (2,048 registers), which does not increase with context length or set cardinality. In a distinct-count experiment involving one million records, the mean relative error was 1.6%. In a separate merge test, states built from as many as 256 segments produced exactly the same readout as a single pass over the same stream. On 3,969 aggregate-then-reason tasks from 174 source windows, the fixed-budget interface reached 99.2% accuracy on Gemma 4 (31B, BF16), compared with 100.0% under exact aggregation; the paired gap was 0.8 percentage points (95% window-cluster CI: 0.5-1.3 points). On a matched set of 174 items, our method improved over direct full-context
reasoning by 63.2 points on Qwen and 56.3 points on Gemma. The corresponding gains over chain-of-thought (CoT)
reasoning were 60.9 and 63.2 points, respectively. On a fixed 1,200-task Oolong-Synth subset, our method reached 91.1% on Qwen and 99.3% on Gemma. Code is available at https://github.com/songdc98/sketchops.
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All authors Dachuan Song, Junyu Yin, Zechen Hu, Xuan Wang
Artificial Intelligence · Cross-listed from cs.RO
Reinforcement Learning on Cost-Constrained Quadrupedal Hardware
Javier C. Weddington, Bence P. Ölveczky and Stephen A. Baccus
Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap. The chasm of simulation to deployment in hardware lies in the delay of the actuator reaching the commanded position. On platforms such as the Mini Pupper 2, a measured >50 ms transport delay transforms the locomotion task from a standard Markov decision process into a partially observable one. In this paper, we take a biologically inspired approach of handling noisy and delayed feedback to close the sim-to-real gap, thereby expanding the capability of reinforcement learning on cost-constrained hardware. Using a low-cost quadrupedal hardware platform, we find that using a forward model of the average actuator delay, paired with a time-aware neural network results in robust locomotion. Additionally, our time-aware neural network learned a central pattern generator (CPG): a self-sustaining rhythmic gait that is robust to +320 ms latency perturbations, mirroring the CPGs found in the spinal cords of vertebrates. We posit that temporal self-organization may be a general strategy for cost-constrained locomotion.
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Artificial Intelligence · Cross-listed from cs.CV
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
Hongyang Wang, Yichen Shi, Hongrui Li et al. (+3 more)
Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence for human inspection. Existing discriminative FAS models remain largely label-centric, while recent MLLM-based methods offer structured outputs but still rely mainly on supervised fine-tuning, often producing template-like rationales and weak optimization for difficult attacks. We propose FAS-R1, a two-stage
reasoning-oriented MLLM framework for unified FAS prediction, covering authenticity classification, attack-type recognition and spoof-region localization. FAS-R1 first uses FAS-R1-23K, a high-quality long-CoT
dataset, for cold-start supervised fine-tuning, and then performs FAS-specific GRPO post-training. Degradation-Simulated Augmentation (DSA) encourages stable spoof-cue
reasoning across visual-quality shifts, while Difficulty-Aware GRPO (DA-GRPO) mitigates easy-sample dominance that may leave difficult task–attack groups under-optimized, especially for subtle or ambiguous attacks such as makeup and mask attacks. The main 3B FAS-R1 model achieves 98.75% authenticity accuracy, 93.33% attack-type accuracy, and 96.30/94.73% AP@40/AP@50 in-domain. It also outperforms the compared systems in cross-domain authenticity generalization and answer-and-rationale quality. Experiments with different base models further show favorable scaling behavior. The code will be released soon.
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All authors Hongyang Wang, Yichen Shi, Hongrui Li, Yiru Huo, Jun Feng, Zitong Yu
Artificial Intelligence · Cross-listed from cs.CL
Voice Memory for Agentic Speech Recognition
Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko et al. (+3 more)
We present Voice
Memory, a inference-only scheme for agentic
speech recognition: at stream time, a frozen corrector reads a single per-domain
memory.md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file through bounded edits, accepting an edit only when it strictly improves a held-out score. Extended from classical ASR-LM framework, we refer this split the listener-thinker architecture; the two roles are coupled only through the
memory, so no weights change and the learned skill stays auditable and portable. Restraint turns out to be the operative skill this loop discovers: unconstrained generative error correction (GER) over-corrects, breaking correct
tokens on up to 64% of its edits on financial news, and Voice
Memory, reduces this rate to 35%. Across ten HyPoradise domains with an open corrector, Voice
Memory, lowers weighted word error rate from 8.36% to 7.52% (7.47% with three added in-context examples) without regressing any
dataset below its 1-best baseline; gains concentrate where recoverable headroom is largest, including air-travel commands (8.40% to 3.40%) and noisy far-field
speech (CHiME-4, 12.69% to 10.46%). The
memory transfers across corrector families and adds zero parameters to the inference path. A demo and example code are provided for future studies.
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All authors Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko, Zhehuai Chen, Jagadeesh Balam, Boris Ginsburg
Artificial Intelligence
CaM-Wolf: Causal-Aware Multimodal Agents for Social Deduction Games
Zheng Zhang, Nanjie Yao, Jiarui He et al. (+3 more)
Social deduction games (SDGs) such as Werewolf have become challenging testbeds for AI
agents. These games require complex social skills such as
reasoning, deception, and collaboration. While recent advances in large
language models (LLMs) have driven significant progress in SDG
agents, current approaches are predominantly text-based, overlooking the
multimodal nature that is fundamental to human social interaction. To bridge this gap, we introduce CaM-Wolf, the first SDG
agent that integrates
multimodal perception and generation. CaM-Wolf processes
video inputs from other players, employs a causal-aware Reasoner trained via reinforcement learning to establish logical chains between observable behaviors and hidden roles, and presents itself through an animated avatar. Our experiments and user study show that CaM-Wolf achieves superior
agent gameplay performance and enhances the quality of human-AI interaction. This work represents a significant advancement towards creating more human-like AI
agents capable of participating in nuanced social dynamics. Our code is available at https://3dagentworld.github.io/avatar_wolf.
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All authors Zheng Zhang, Nanjie Yao, Jiarui He, Deheng Ye, Peilin Zhao, Hao Wang
Artificial Intelligence · Cross-listed from cs.CL
Misalignment Has a Personality: A Big Five Account of Emergent Misalignment
Hasibur Rahman and Smit Desai
Fine-tuning a
language model on data containing a narrow flaw, such as insecure code or incorrect mathematical answers, can cause broad misalignment through a mechanism that remains debated. We provide an interpretable account: in the models and corpora we study, misalignment behaves like a shift in personality. Prior work extracts activation directions for character traits from a single binary contrast, which can separate or steer behavior without establishing a calibrated scale. We instead extract personality vectors for the Big Five using a graded, three-level intervention and validate them on two open-weight models. The three levels are linearly ordered, with Cohen's d values of up to 6.2; the vectors transfer zero-shot and trait-specifically to an independent corpus; and their effects are strongest within a middle-layer band. Applied to training data, the vectors reveal that misaligned corpora across eight domains share a common Big Five signature: lower agreeableness and conscientiousness, together with higher extraversion and neuroticism. This signature is recovered by both models with a correlation of r = 0.94. Fine-tuning imprints the same profile, shifting the model's generations along the corresponding signature, with r = 0.83 using activation-based measurements and r = 0.90 using a text-based judge, while also shifting internal activations with r = 0.69. The same vectors characterize sycophancy as high extraversion and low conscientiousness rather than excess agreeableness, a distinction that a single direction cannot capture. Calibrated personality vectors transform an opaque safety phenomenon into a human-legible diagnostic profile.
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Artificial Intelligence · Cross-listed from cs.GT
Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
Xin Xu, Chengrui Wu, Jiayu Lu et al. (+3 more)
Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered "no" by a conspiracy that is nonetheless profitable. Consider bidding
agents that couple only through the joint distribution of their unexplained bid components, leaving every
agent's own bid law exactly at the competitive law. Any test whose input is a single
agent's price or bid history then has power exactly equal to its false-positive rate, for every coupling strength up to comonotonicity. The published detection methodology is therefore blind to this conduct by construction rather than underpowered, and no sample size repairs it. Three empirical results follow. First, the mechanism appears in real
language-model
agents: twenty models from nineteen independent developers, three deployment
prompts each, show residual correlation of $+0.053$ between two deployments of one model against $+0.0001$ across models, with a 95% interval clustered by developer of $[0.030, 0.078]$, under an auditor that sees every order feature and is fitted out of sample. Second, the coupling falls monotonically as sampling temperature rises ($p=0.002$), turning a deployment parameter into a candidate mitigation. Third, on 24 days of Ethereum block-building auction data covering 77,684 bids from 39 bidders, the honest population of bidder pairs is itself so dependent that a screen held at a 5% false-positive rate must sit above a floor of $+0.50$ to $+0.81$, which is 20 to 32 times the family-wise sampling threshold and does not fall as the audit window grows. Since lawful multi-identity operation and conspiracy are behaviourally indistinguishable here, the tractable regulatory target is not detection but counting: resolving 40 bidding identities into 23 operators raises the Herfindahl index by 247.5%, and adding behavioural clusters from public bid streams reaches 324.5%.
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All authors Xin Xu, Chengrui Wu, Jiayu Lu, Kaizhen Tan, Siru Tao, Hanzhe Hong
Artificial Intelligence · Cross-listed from cs.CV
Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
Yitong Shen, Cheng Guo, Peiliang Wang et al. (+5 more)
Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities. We present Zero-Fi, a contrastive signal-
language alignment framework for zero-shot Wi-Fi-based human activity recognition. Zero-Fi learns unified representations from complementary Wi-Fi signal features and aligns them with the semantic representations of natural-
language activity descriptions in a shared
embedding space. This cross-modal
alignment enables Zero-Fi to recognize new activity classes without requiring labeled Wi-Fi samples or model adaptation for those classes. Experiments on large-scale public
benchmark datasets demonstrate effective zero-shot recognition of held-out activity classes, highlighting the potential of signal-
language alignment to extend Wi-Fi sensing beyond predefined activity classes.
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All authors Yitong Shen, Cheng Guo, Peiliang Wang, Jingzhe Zhang, Yi Sheng, Haopeng Zhang, Hongfei Xue, Yili Ren
Artificial Intelligence · Cross-listed from cs.CL
Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
Aman Kumar, Lasitha Vidyaratne, Dipanjan D Ghosh et al. (+2 more)
Financial disclosures may contain numerical, temporal, referential, factual, and policy inconsistencies that require different evidence and
reasoning to diagnose. We study fine-grained inconsistency classification: given a passage known to contain a conflict, the goal is to identify its type among 11 categories. Using a fixed snapshot of the synthetic SBID-FD
benchmark, we compare frozen and fine-tuned encoders, evidence-augmented classifiers, prompted large
language models, and LoRA-adapted generative models under a shared evaluation protocol. Task-specific adaptation yields large improvements over frozen representations, and a fine-tuned 300M encoder performs competitively with substantially larger prompted and adapted models. We further study whether localizing the conflicting claims improves classification through matched predicted-span, reference-span, and distractor-span conditions. The results show that automatically extracted evidence provides additional signal but recovers only part of the benefit obtained from reference spans. Per-class and confusion analyses further reveal that some inconsistency types are especially sensitive to localization quality, whereas others remain difficult even when the relevant evidence is supplied. These findings identify evidence localization and fine-grained type discrimination as distinct challenges and show that compact supervised encoders are strong baselines for this task.
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All authors Aman Kumar, Lasitha Vidyaratne, Dipanjan D Ghosh, Arnab Chakrabarti, Ahmed K Farahat
Artificial Intelligence
Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?
Wanyu Zhao and Wanbing Zhao
An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-
agent task: can LLM-based
agents discover statistical mechanical mappings from a raw partition function to a tractable representation? To probe this question, we introduce StatMechBench-v0, a
benchmark of six Ising-type problems covering transfer-
matrix methods, gauge-removable disorder, and planar/Pfaffian structure. We evaluate a simple propose-verify-revise
agent across multiple LLMs and problem phrasings. The results show that numerical feedback often helps
agents repair code and recover correct partition functions. However,
agents can also pass the numerical checks while misidentifying the underlying tractable class or understating computational complexity. This both reveals limitations in current LLM
reasoning and calls for a verification stack that goes beyond numerical agreement, incorporating, for example, symbolic checks and structural invariants. Our study provides an early evaluation and design directions for AI
agents aimed at structural discovery in theoretical physics.
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