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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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d 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.

arxiv1d agoScore 88
multi-agent systemsgeospatial analyticsdisaster response
Research

Mobile AI 2026 Challenge: Learned Smartphone ISP on Mobile GPUs

This arXiv paper reports on the Mobile AI 2026 challenge, which tasked participants with designing deep learning models to reconstruct RGB photos from RAW Bayer data on smartphone GPUs. Using the Fujifilm UltraISP dataset with Sony IMX586 sensor and GFX100 camera pairs, top solutions achieved high-quality 5MP reconstruction in under 100 ms on Snapdragon and Dimensity chipsets. The paper provides a comprehensive description of the winning models.

arxiv1d agoScore 78
computational photographyefficient deep learningmobile hardware
Research

CoGA2M: A Composite GAM That Beats LightGBM Under Distribution Shift

This paper introduces CoGA2M, a multi-resolution, higher-order Composite Generalised Additive Model, and benchmarks it on the TALENT suite. CoGA2M ranks in the top third across all baselines on regression and classification tasks, matching state-of-the-art tabular methods. Under new out-of-distribution splits, CoGA2M outperforms LightGBM, highlighting a structural advantage of additive models in distribution shift.

arxiv1d agoScore 78
Generalised Additive ModelsTabular LearningDistribution Shift
Research

Mobile AI 2026 Challenge Report: Efficient RGB Photo Enhancement on Smartphone GPUs

This paper presents the Mobile AI 2026 challenge on on-device RGB photo enhancement, targeting efficient deep learning models for smartphone GPUs. Participants used the DPED dataset and optimized for Qualcomm and Arm chipsets, with top solutions enhancing Full HD images in under 300 ms. The report details the models and results, highlighting practical deployment on constrained hardware.

arxiv1d agoScore 75
Mobile AIImage EnhancementOn-device Deep Learning
Research Editor selected

ALBI: LLM-Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refinement

ALBI is a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures paradigm with an LLM, while using SWI-Prolog as a formal verifier. It implements an iterative generate-test-constrain loop where the LLM proposes hypotheses, Prolog tests them, and diagnostic feedback drives refinement. On 30 standard ILP benchmarks, ALBI achieves 100% success across three frontier LLMs, matching Popper with only 1.4-1.7 iterations on average. Semantic anonymization tests confirm robust symbolic reasoning, with all models maintaining 100% success despite a moderate increase in iterations.

Why it matters: This paper presents a novel integration of LLMs with formal verification for ILP, demonstrating high success and robustness, which is valuable for researchers and practitioners in neuro-symbolic AI.

arxiv1d agoScore 82
Inductive Logic ProgrammingLarge Language ModelsNeuro-symbolic AI
Research Editor selected

Geometric Abstraction Reduces Multimodal Queries in Agentic Robot Policies

This paper proposes replacing step-by-step visual reasoning in multimodal robot policies with a 3D geometric abstraction that converts scene geometry into typed, evidence-aware symbolic references grounded from RGB-D data. The system refreshes metric values locally and returns compact textual checkpoints after each action, invoking direct visual reasoning only on demand. Experiments show improved multi-step manipulation completion while substantially reducing repeated multimodal queries.

Why it matters: Offers a concrete method to improve efficiency and reliability of agentic robot policies by using geometric abstraction, relevant to AI-driven robotics.

arxiv1d agoScore 85
robot manipulationmultimodal language modelsgeometric reasoning
Research Editor selected

FloMo: 3D Scene Flow as a World Action Model Intermediate for Learning from Human Video

FloMo is a 5B video-generation model fine-tuned to predict 3D scene flow and action tokens, using scene flow as an intermediate representation to transfer video pretraining to robot action prediction. Co-trained on human video and robot teleoperation data, it generalizes to unseen objects and tasks, outperforming video prediction objectives. Experiments show that predicting video wastes model capacity, as action tokens attend more to motion than video.

Why it matters: This paper presents a concrete, recent AI development with empirical evidence that 3D scene flow improves sample efficiency and generalization in robot learning from video.

arxiv1d agoScore 85
Robot learningVideo pretrainingScene flow
Research

VIGOR: Visual Gain Ordering for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models

VIGOR is a training-free, plug-and-play inference strategy that reduces hallucination in multimodal discrete diffusion language models by reordering masked token positions based on counterfactual visual evidence. It compares confidence under original and visual-ablation passes to prioritize positions strongly supported by the image. Experiments on LLaDA-V and Lumina-DiMOO show consistent hallucination reduction and improved multimodal reasoning.

arxiv1d agoScore 78
multimodal diffusionhallucination mitigationtoken ordering
Research Editor selected

RFWorld: Rollout-Free World Model for End-to-End Autonomous Driving

RFWorld introduces a rollout-free world model for trajectory evaluation in end-to-end autonomous driving. It builds a shared scene memory and uses lightweight training-time temporal readout with semantic supervision to create future-readable representations, avoiding costly recursive rollout. At inference, candidates query the memory via sparse spatial readout, cutting computation and improving planning-aligned representation, achieving SOTA on NAVSIM and Bench2Drive.

Why it matters: This is a concrete research advance that reduces inference cost and improves trajectory evaluation accuracy, with benchmark results on standard driving datasets.

arxiv1d agoScore 85
end-to-end planningworld modelstrajectory scoring
Research Editor selected

LatentTriad: Pre-Action Risk Representations for Malicious Agent Skill Detection

LatentTriad is a new method that detects malicious skills in LLM agents without executing them, using hidden states from a single prefill pass. It combines contextual boundary features, group-adjusted operation directions, and departures from benign state relationships to achieve 99.0-99.2% pooled F1 across 5,830 skill packages. The method is execution-free, producing no output tokens and invoking no tools, with a compact implementation running in 259-284 ms median.

Why it matters: This paper presents a concrete, recent AI safety technique with strong empirical results and practical efficiency, valuable for developers and researchers working on agent security.

arxiv1d agoScore 85
AI safetyLLM agentsmalicious skill detection