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Research

Network Models of Science Compare Pluralist vs. Monist Agents in Belief Dynamics

This paper introduces a multi-agent variational inference model to test Feyerabend's theoretical pluralism, comparing communities of agents that can hold rival theories simultaneously against monist agents that maintain a single model. Experiments show how pluralist communities undergo uncertainty and theory shifts, how inter-community communication affects beliefs under differing observations, and how sharing theory support versus sharing beliefs within theories differently shape collective dynamics. The work provides a computational framework for studying scientific belief evolution.

arxiv1d agoScore 72
multi-agent variational inferencebelief dynamicsscientific communities
Research

Deep Learning Polyp Matching for Colon Capsule Endoscopy

This study introduces a contrastive learning-based Siamese neural network for matching polyps across colon capsule endoscopy images, trained on 5,103 same-polyp and 5,103 different-polyp pairs from the Danish CareForColon2015 trial. Using ResNet, DenseNet, and EfficientNet backbones with cosine distance, the model achieves up to 92% accuracy and AUC near 0.98. The approach aims to reduce polyp over-counting and unnecessary follow-up colonoscopies.

arxiv1d agoScore 78
polyp matchingcontrastive learningcolon capsule endoscopy
Research

QueryFlow: Multi-Hop Graph Retrieval with Adaptive Query Evolution

QueryFlow is a new retrieval method for multi-hop RAG that jointly evolves evidence representations and query conditions. It integrates query-conditioned summaries of graph context into a shared query state to guide subsequent propagation. On four multi-hop QA datasets, it improves Recall@10, Full Recall@10, and LLM-Acc by 4.98, 8.40, and 8.0 percentage points over baselines.

arxiv1d agoScore 78
retrieval-augmented generationgraph-based retrievalmulti-hop question answering
Research

Novel SAT Encoding for Ladder At-Most-K Cardinality Constraints

This paper introduces a new SAT encoding for sequences of At-Most-K (AMK) cardinality constraints arranged in a ladder-like structure, called Ladder AMK. The encoding decomposes the constraint sequence into blocks, encodes each block using a Sequential Counter, and links blocks with clauses, reducing variable and clause counts compared to prior BDD-based encodings. Experiments on single constraints and real-world problems (Anti-bandwidth and Nurse Rostering) show improved solving efficiency over existing SAT, CP, and MIP solvers.

arxiv1d agoScore 72
SAT solvingcardinality constraintscombinatorial optimization
Research Editor selected

LinUCB Works Without Valid Confidence Sets on Curved Action Sets

This paper shows that LinUCB's regret remains sublinear even when its confidence width is replaced by any fixed positive constant, provided the action set is smooth and strongly convex. The authors introduce a logarithmically capped spectral potential to analyze exploration driven by action-set curvature. Their regret bound improves the horizon dependence of classical LinUCB by a factor of sqrt(log T).

Why it matters: Provides a theoretical breakthrough in bandit algorithms, showing valid confidence sets are unnecessary under geometric conditions, with improved regret bounds.

arxiv1d agoScore 82
online learningbandit algorithmsregret analysis
Research Editor selected

Therapy-FIS: First Public Dataset for Automated Assessment of Therapist Interpersonal Skills

Therapy-FIS is a new dataset for automated assessment of Facilitative Interpersonal Skills (FIS) in psychotherapy, containing facial, acoustic, and textual signals from 890 therapist recordings and 8 patient videos. It includes eight FIS subscale ratings and an overall score, and supports two benchmark tasks: Therapist Performance Rating and Joint Therapist–mock-Patient Performance Rating. The authors also introduce a baseline model, DyadicFIS, which achieves best macro CCC scores of 0.2807 and 0.3299 on the two tasks.

Why it matters: This is the first publicly available dataset for automated FIS assessment, addressing a critical data scarcity and enabling reproducible research in mental health AI.

arxiv1d agoScore 85
Facilitative Interpersonal SkillsAutomated assessmentMental health AI
Research

BoD: Backpropagating Through Depth Improves Looped Language Model Code Generation

This paper introduces Backpropagation through Depth (BoD), a training method for looped language models that backpropagates through all intermediate depths rather than only the final depth. BoD improves code-generation success rates, reaching 91.03% coverage on N-Queens versus 45.75% for final-depth training, and also enhances generalization to deeper computations and withheld solutions. The method shows promise for training recurrent computation for solution discovery.

arxiv1d agoScore 72
Backpropagation through DepthLooped language modelsCode generation
Research Editor selected

Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation

This paper introduces UAPA, a plug-and-play method for open-set test-time adaptation that handles both covariate-shifted in-distribution and out-of-distribution samples. It combines uncertainty-driven ID/OOD separation, dynamic prototype refinement, and contrastive prototype entropy adaptation to improve adaptation reliability. Experiments on CIFAR benchmarks and Tiny-ImageNet-C show average OSCR improvements of 2.08 and 1.28 percentage points over strong baselines.

Why it matters: Presents a novel, concrete method with empirical gains for a challenging open-set TTA problem, useful for researchers and practitioners.

arxiv1d agoScore 82
Test-Time AdaptationOpen-Set RecognitionPrototype Learning
Research Editor selected

When Ground Truth Is Not Ground Truth: Automatic Labeling for Hallucination Detection

This paper investigates the reliability of automatic labeling methods for hallucination detection in LLMs, showing that using similarity metrics like BLEURT as proxies for ground truth can be highly unreliable. Through experiments with 15 judges, 7 detectors, 2 generators, 2 QA datasets, and 5 languages, they find that detector performance varies significantly with judge choice. Human validation shows QGen-9B achieves the lowest misclassification rate (12.86%) while BLEURT and BLEURT-n exceed 47%, highlighting the need for more factuality-aware judging methods.

Why it matters: Provides concrete evidence and benchmarks on the pitfalls of common automatic labeling practices, offering actionable insights for improving hallucination detection evaluation.

arxiv1d agoScore 82
LLM hallucinationevaluation methodologyautomatic supervision
Research

CLARA: A Cognitively-Driven Clarification Framework for Grounding Ambiguous Requests

CLARA is a clarification framework for large vision-language models in robotics that proactively asks users targeted questions to resolve ambiguous instructions like 'clean the room'. It converts vague requests into structured, detailed instructions and uses a diffusion-based pipeline to render the final scene for visual confirmation. Experiments and user studies show CLARA outperforms a state-of-the-art LVLM system in accuracy and consistency.

arxiv1d agoScore 78
CLARAambiguous instruction groundinginteractive clarification
Research Editor selected

DenseTwins: Dense Modality Decoupling for Multimodal Self-Supervision

DenseTwins is a new multimodal self-supervised framework that learns dense representations by explicitly separating shared and modality-specific features. It combines dense cross-modal correspondences with redundancy-reduction objectives, optimizing both intra-modal and cross-modal losses. The method achieves state-of-the-art or competitive results on multiple multimodal benchmarks and sensing setups, with code released.

Why it matters: This paper presents a concrete, recent AI development with a novel approach to multimodal representation learning, offering practical value for robotics and autonomous driving.

arxiv1d agoScore 82
Multimodal Self-Supervised LearningDense Representation LearningModality Decoupling
Research

Cycle WAM: Coupling Action and Video through Cycle Consistency

Cycle WAM introduces a unified training framework that treats video and action as paired modalities linked by causality, using bidirectional conversion and a differentiable cycle-reconstruction objective to enforce consistency between forward and inverse dynamics. It achieves state-of-the-art results on LIBERO-Pro and improves DreamZero on DROID, with sharper visual detail in forward-dynamics predictions compared to UWM.

arxiv1d agoScore 78
World Action ModelsVideo ModelingRobot Learning
Research

TAPES: Task-Aligned Joint Predictive Pretraining for Transaction Event Sequences

TAPES is a pretraining framework for transaction event sequences that selects predictive target sets based on downstream utility and models joint likelihood via a shared discrete latent variable. It outperforms baselines on real-world and public financial benchmarks, with ablations confirming the value of target selection and joint modeling.

arxiv1d agoScore 78
transaction event sequencespredictive pretrainingfinancial benchmarks
Research

Input-Dependent Activation Steering Outperforms Fixed Directions When Destination Varies Within Class

This paper investigates when activation steering edits should depend on the input, proposing that input-dependence is needed exactly when the destination varies within the requested class. The authors compare fixed-direction Contrastive Activation Addition (CAA) with a retrieval-based method that uses the mean of k nearest stored states, finding retrieval outperforms fixed steering on substitution corpora when within-class variation exists. The study also identifies four state-derived quantities that predict the appropriate regime.

arxiv1d agoScore 78
activation steeringcontrastive activation additioninput-dependent editing
Research

Replica-Aware Split Inference for Edge IoT Systems

This paper proposes a replica-aware recovery approach for split inference in IoT systems, where replicas of TinyML model segments from failed devices are placed on surviving devices. The joint selection of replica hosts and communication protocols is formulated as a mixed-integer linear programming (MILP) problem to minimize post-reconfiguration inference latency under memory constraints. Simulations with MobileNetV2 show the MILP approach achieves 6.8% lower latency than Computation Greedy and 18.3% lower than Random.

arxiv1d agoScore 72
Split InferenceTinyMLEdge Computing
Research Editor selected

Logprob Reduction Fails to Verify Causal PII Suppression in LLMs

This paper shows that SAE-based feature interventions for suppressing PII leakage in LLMs, when evaluated only by log-probability reduction, often produce false positives. Across 36 cases, 47% appeared successful under logprob-only evaluation, but only 2.8% passed a multimetric framework with random direction controls. The authors identify two failure modes and propose causally grounded PII suppression via circuit-level localization and causal mediation analysis.

Why it matters: Provides concrete evidence that a common evaluation metric is unreliable)Skip and proposes a more rigorous methodology, directly impacting AI safety research.

arxiv1d agoScore 85
sparse autoencodersinterpretabilityprivacy
Research Editor selected

Kierston & KierstonBench: Simulated City Benchmark for AI in Crime Investigation

The paper introduces Kierston, a city-scale simulation of Allentown, PA, and KierstonBench, a benchmark for evaluating language models in crime investigation and surveillance tasks. It generates realistic records from a common behavioral timeline and tests models on 32 task families. Evaluation of 16 models shows most fail, while a few frontier models solve most tasks but still fall for deceptions.

Why it matters: Provides a novel, concrete benchmark for a critical AI application area, with clear evaluation results and practical implications.

arxiv1d agoScore 85
AI safetycriminal investigationlanguage model evaluation
Research

ReSI-Math: Recoverable Capability Frontiers for Efficient Reasoning Improvement

ReSI-Math is a framework that identifies recoverable reasoning failures in LLMs—cases where direct decoding fails but trajectory-level search can elicit correct solutions—and converts them into efficient RLVR supervision. Using self-judge-guided evolutionary search, it selects frontier problems for sequential RLVR stages. On Qwen3-VL-8B, it improved MATH500 and WE-MATH averages from 78.71 to 80.16 after three stages, and frontier-guided selection outperformed random selection in matched single-problem RLVR.

arxiv1d agoScore 78
reinforcement learningLLM reasoningdata efficiency