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Research Editor selected

PaySeqGen: Interpretable Inverse Reinforcement Learning for Fraud Sequence Generation

PaySeqGen is an interpretable inverse reinforcement learning framework that reconstructs fraud behavior policies from observed payment trajectoriesholistically, generating new fraud sequences for detector augmentation. It learns interpretable behavioral rewards capturing fraudsters' preferences across transaction contexts)Skip, and experiments at Ant International show it outperforms state-of-the-art generative augmentation methods. The framework also enables reward-based trajectory analysis to identify multistep fraud tactics in real payment systems.

Why it matters: This paper presents a novel application of inverse RL to payment fraud, with real-world deployment and interpretability benefits, offering practical value for fraud detection and analysis.

arxiv4d agoScore 82

fraud detectioninverse reinforcement learningpayment security

Research

UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation

UniFlow is a new framework for time series generation that integrates multi-scale representation learning with non-autoregressive generation via a shared-semantic discrete interface. It uses a multi-scale tokenizer with shared-index voting to consolidate temporal information into a single public-ID sequence, and geometry-aware categorical flow matching to generate these IDs efficiently. Experiments on four benchmarks show improved fidelity, cross-scale consistency, and faster inference than autoregressive and diffusion baselines.

arxiv4d agoScore 78

time series generationflow matchingmulti-scale representation learning

Research

Feeling of Knowing in Large Language Models

This paper introduces a functional 'feeling of knowing' (FoK) mechanism for LLMs to align answerability judgments with post-training knowledge updates. It proposes two trainable channels—knowledge and FoK—trained via three stages: on-policy supervised fine-tuning, knowledge-delta FoK training, and FoK-guided policy optimization. Experiments on factual QA and sequential updates show improved judgment quality without sacrificing capability on MMLU and ARC-Easy.

arxiv4d agoScore 78

LLM alignmentknowledge judgmentmodel routing

Research Editor selected

HTCE: Topological Cross-Scene Experience Reuse for Object Navigation

HTCE is a homeomorphism-inspired framework that canonicalizes and reuses navigation experience across scenes for embodied object-goal navigation. It compresses stored route descriptions by 17.7–23.6% without performance loss and achieves state-of-the-art success and SPL on HM3D and MP3D benchmarks under a frozen vision-language policy. The method improves success rate by up to 4.9 points and SPL by up to 2.8 points.

Why it matters: This paper presents a novel topological approach to experience reuse in embodied AI, with concrete benchmark gains and open-source code, offering practical value for navigation research.

arxiv4d agoScore 82

Object Goal NavigationTopological LearningExperience Reuse

Research

Searched Intrinsic Rewards Transfer but Are Tamperable by a Shortcut

This paper investigates whether intrinsic rewards found by evolutionary search over generic event combinations can transfer to unseen tasks and resist exploits absent during search. The CMA-ES-selected reward beat task-only PPO and random-search reward on a held-out task, but a hand-wired reward with task-specific events performed better. A free reset action, not present during search, collapsed learning, showing selection gave no protection against such exploits.

arxiv4d agoScore 78

Intrinsic reward searchReinforcement learning transferReward tampering

Research

DGC-Net: Cross-Modal Dynamic Routing with Global-Guided Compensation in Multimodal Sentiment Analysis

DGC-Net is a new network for multimodal sentiment analysis that addresses cross-modal affective cue inconsistency and global-local contribution imbalance. It uses cross-modal contribution-aware dynamic expert routing with global contexts to guide local expert selection, and global-guided dynamic compensation fusion to adaptively combine global semantics with refined local cues. Experiments on five multimodal benchmarks show effectiveness and general applicability.

arxiv4d agoScore 78

Multimodal Sentiment AnalysisDynamic GatingGlobal-Guided Compensation

Research Editor selected

MambaPFN: Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks

The paper introduces MambaPFN, a Transformer-based method for model-agnostic variable selection that identifies variables with nonlinear dependence on a response. It uses a functional-mapping perspective, pretraining on generated data to learn posterior inclusion probabilities, then applies to new datasets without updates. Experiments show strong performance, including a scaling law and a 20% improvement over baselines on a Facebook comments dataset.

Why it matters: Presents a novel, concrete AI method with empirical gains and scaling behavior, useful for researchers and practitioners in interpretable ML.

arxiv4d agoScore 82

variable selectionTransformer architecturesamortized learning

Research Editor selected

GroupAssistBench: Benchmarking Real-World Group Memory Assistance Beyond Recall

GroupAssistBench is a new benchmark for evaluating memory use in group assistance, built from real-world group conversations with a deployed assistant, covering 80,299 messages across 50 groups. It tests both recall (L1) and use (L2) with service requests lacking fixed answers, across single, distributed, and conflicting memories. Evaluations show frontier models and memory systems lag on use, especially for distributed and conflicting memories, but adding retrieval-relation and constraint-binding techniques improves performance.

Why it matters: This introduces a novel benchmark addressing a gap in AI memory evaluation, with real-world data and actionable insights for improving group assistant systems.

arxiv4d agoScore 85

AI memorygroup chat assistantsbenchmarking

Research Editor selected

ScamWorld: Interactive Environment for Evaluating Web Agents Against Online Fraud

ScamWorld is a new interactive environment that reconstructs reported scam websites to test how web agents handle online fraud. In tests with six frontier LLMs, baseline deception rates ranged from 11% to 35%, with agents more likely to proceed with payments than with credential or wallet requests. The environment enables controlled, repeatable evaluation of agent behavior against real-world fraud scenarios.

Why it matters: Provides a concrete, novel testbed and empirical results on LLM-based web agents' vulnerability to online fraud, directly relevant to AI safety and agent evaluation.

arxiv4d agoScore 85

AI safetyweb agent evaluationonline fraud

Research Editor selected

Modelling lock-in of AI adoption through skill erosion

This paper presents a mathematical model showing that generative AI can improve immediate performance but erode human skills, leading to bistability and hysteresis between low-adoption/high-skill and high-adoption/low-skill states. The model derives exact skill distributions for users and non-users, and shows that AI adoption can become locked in due to history-dependent switching costs. It also finds asymmetric transitions: a single AI user can spread among experts if AI pay exceeds expert pay, while a single non-user can only reverse adoption if they regain sufficient skill before returning to AI.

Why it matters: Provides a rigorous theoretical framework for understanding AI adoption lock-in and skill erosion, with practical implications for policy and workforce planning.

arxiv4d agoScore 82

AI adoption dynamicsskill erosionhysteresis

Research

FourSight: Stable FP4 RL Post-Training via Sequence Filtering and Sparse Channel Compensation

FourSight addresses instability when applying NVFP4 W4A4 quantization to reinforcement learning post-training. It identifies that failures concentrate in outlier sequences and high-variance activation channels, then applies TIM-aware sequence filtering and sparse BF16 channel compensation. The method sustains near-BF16 accuracy and improves step-to-accuracy by 1.76-2.60x while staying within 2.3% of FP4 step time up to 120B-scale models.

arxiv4d agoScore 78

quantizationreinforcement learningmodel deployment

Research Editor selected

Fault-tolerant shallow recurrent decoders for sparse sensing with missing sensor measurements

This paper introduces fault-tolerant sparse sensing strategies for shallow recurrent decoders (SHRED) to handle missing, asynchronous, and heterogeneous sensor data. The authors propose transformer-based masking mechanisms and a multi-step decoding training objective, demonstrating improved reconstruction fidelity and robustness across synthetic and real-world hydrodynamic/ocean modeling benchmarks without added inference overhead.

Why it matters: Provides concrete, recent advances in robust AI-based state estimation for real-world sensing, with clear practical value for operational environments.

arxiv4d agoScore 82

SHREDsensor fault tolerancetime-series reconstruction

Research Editor selected

VisSearch-Bench: New Benchmark Evaluates Long-Horizon Visual Search in Multimodal Agents

VisSearch-Bench introduces 204 human-written tasks across 7 domains to evaluate multimodal agents on visual context management during long-horizon search. It covers five task types requiring agents to combine visual details with external information. Benchmarking 16 models shows the best system solves only 26.47% of tasks, highlighting significant room for improvement.

Why it matters: Provides a concrete, recent benchmark with clear results that quantify current limitations in multimodal agent visual search, valuable for researchers and developers.

arxiv4d agoScore 85

multimodal agentsvisual searchbenchmarking

Research Editor selected

Artifact-to-Capability: Diagnosing Breaks in Capability Formation of Self-Evolving Agents

This paper introduces a formal definition of capability formation from artifacts in self-evolving agents, proposing five sequentially dependent properties and a three-scale diagnosis method. In freestyle Gomoku, Reflexion, Voyager, and DGM all show standard proxy signals but exhibit distinct capability-formation breaks, such as legalization gain, compositional override, and loss-only drift. The findings demonstrate that signal-based evaluation cannot reliably determine whether a capability has formed.

Why it matters: Provides a concrete diagnostic framework and counterexamples that challenge common evaluation practices in self-evolving AI agents, offering practical value for researchers and developers.

arxiv4d agoScore 85

self-evolving agentscapability evaluationagent diagnostics

Research Editor selected

Shuffled OPSD: When Semantic Consistency Hurts On-Policy Self-Distillation?

This paper introduces Shuffled OPSD, a variant of on-policy self-distillation that shuffles the words of the shared problem statement while keeping the teacher-only worked example intact. Across three math reasoning benchmarks and three Qwen3 model scales, it improves mean accuracy over both intact-target and Standard OPSD, with the largest gain of 3.52 points on Qwen3-1.7B. Controls show that shuffling the teacher-only example hurts accuracy, while word-level shuffling of the problem statement helps, challenging the assumption that semantic coherence is always beneficial.

Why it matters: Provides concrete, recent experimental evidence that disrupting input structure can improve self-distillation, offering a novel insight for AI training methodology.

arxiv4d agoScore 85

on-policy self-distillationknowledge distillationmathematical reasoning

Research

LLMs Outperform Most Humans in Pronoun Disambiguation but Need Human Oversight

A study compared 73 human annotators and over 40 LLM configurations on disambiguating 174 authentic uses of 'they' from Reddit. The best LLM, Kimi K3, achieved 94.5% accuracy, surpassing the human majority vote (86.2%) and the top-half human mean (78.5%), though the single best human scored 95.7%. LLMs offered cost and speed advantages, but qualitative analysis showed human reasoning in low-agreement cases improves the annotation typology, arguing for human oversight rather than full replacement.

arxiv4d agoScore 78

Large Language ModelsNatural Language ProcessingHuman-in-the-loop

Research Editor selected

ARPG: Dependency-Aware Agentic Framework for Automated Robot Policy Generation

The paper introduces ARPG, a framework that uses LLMs as designers to automatically generate executable robot reinforcement learning policies from natural-language task descriptions. It decomposes the process into sequential MDP formulation and modular simulator implementation, with localized verification and correction to ensure semantic alignment of state, action, reward, and termination. Experiments in Isaac Sim show improved modeling correctness, environment executability, and policy-generation success over baselines.

Why it matters: Provides a concrete, recent AI development with a novel framework and experimental validation, useful for researchers and practitioners in robot learning and LLM agents.

arxiv4d agoScore 82

automated policy generationLLM as designerdependency-aware framework

Research Editor selected

GeoFaham: Multi-Agent Geospatial Analytics for Disaster Response

GeoFaham is a domain-specialized multi-agent system that decomposes geospatial disaster-response workflows into specialist agents for vector, map, imagery, and raster analysis, coordinated by an Orchestrator with a dual-ledger control loop. It uses a Response Store to persist outputs outside the LLM context, reducing token usage. On the new GeoFaham-Bench benchmark, it outperforms general coding agents with a score of 0.940 versus 0.879, using 7.5 times fewer tokens.

Why it matters: Introduces a novel multi-agent architecture and benchmark for geospatial AI, showing significant efficiency and accuracy gains over strong baselines.

arxiv4d agoScore 88

multi-agent systemsgeospatial analyticsdisaster response