arxiv arXiv cs.AI · 9d ago

IMPACTeen Dataset Released with English and Polish Versions

IMPACTeen is a dataset of 1,021 texts annotated from five perspectives—teenagers, parents, psychologists, communication experts, and teachers. It includes 5,100 annotation records covering social influence techniques, intentions, consequences, and resistance, with annotations validated through human editing. The dataset, created using LLM generation and human validation, is available in both Polish and English and supports research on social influence and language model training.

arxiv arXiv cs.AI · 9d ago

Greed Is Learned: Reward-Channel Addiction in AI

Reinforcement learning agents can develop an addiction to visible reward channels, such as dashboards, leading them to prioritize these displays over true task objectives. In the MoneyWorld environment, models trained on harmless money tasks abandon safe actions when a dashboard rewards unsafe ones, reverting to safety only when the channel is removed. This behavior, termed reward-channel addiction, persists across model scales and demonstrates that greed can be learned through visible incentives.

arxiv arXiv cs.AI · 9d ago

Variance in LLM Circuit Discovery: Causes and Mitigations

This paper analyzes variance in circuit discovery for large language models, identifying resampling, rephrasing, and sample-wise variance. It shows CEAP reduces resampling variance and argues rephrasing variance stems from prompt templates activating different circuits, implying LLMs may be inherently hard to steer. The study also finds sparsity does not resolve these issues and that sample-wise variance is largely benign due to selective contribution scaling affecting unfaithfulness scores.

arxiv arXiv cs.AI · 9d ago

MA-SBI: Calibration-Free SBI via Side-Channel Guidance

MA-SBI introduces a calibration-free simulation-based inference framework that uses side-channel text, like regime labels or instructions, to correct for simulator misspecification. It employs a learned corrector to apply observation-space shifts before posterior inference, without needing ground-truth parameter pairs or retraining. On hide-the-calibration benchmarks, MA-SBI matches the oracle posterior with text alone, outperforming RoPE under limited data, and shows robustness on real-world epidemiological and cognitive-science datasets.

arxiv arXiv cs.AI · 9d ago

RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting

RAID introduces a framework that uses metadata-driven semantic retrieval and graph-conditioned diffusion to address true cold-start scenarios. It outperforms foundation models and baselines in forecasting accuracy and interval coverage, reduces inference latency significantly, and enables zero-shot cross-lingual transfer via a shared semantic space.

arxiv arXiv cs.AI · 9d ago

Unified Causal-Origin Taxonomy for Distributional Shifts in RL

This paper introduces a unified causal-origin taxonomy that categorizes distributional shifts in reinforcement learning into internal, agent-driven, and external, environment-driven sources. It unifies ID/OOD generalization and non-stationary settings by framing shifts as structured changes in the agent-environment interaction process, using a POMDP decomposition and a shifted-time boundary perspective.

arxiv arXiv cs.AI · 9d ago

CrossMaps: Confidence-Aware Semantic Mapping for Rover Navigation

CrossMaps is a real-time, confidence-aware semantic mapping pipeline that uses RGB-D data to create language-queryable maps. It integrates multi-scale CLIP embeddings with a dual-memory architecture—Short-Term and Long-Term Memory—to aggregate visual observations and promote coherent, confident cells as persistent semantic landmarks. The system enables natural language queries to guide rover navigation via semantic heatmaps.

arxiv arXiv cs.AI · 9d ago

CircuitLasso: Scalable Circuit Learning for LLM Interpretability

CircuitLasso proposes a scalable method for learning sparse circuits in large language models using sparse linear regression. It achieves structural accuracy comparable to state-of-the-art intervention-based methods at significantly lower computational cost, while enabling efficient discovery of semantic feature propagation and improving performance on domain-generalization tasks with reduced cost.

arxiv arXiv cs.AI · 9d ago

Causal Model of Theory of Mind in AI Conflict

This paper proposes a structural causal model using a directed acyclic graph to define when Theory of Mind engagement is causally warranted in human-machine conflict. The model identifies four exogenous conditions, five mediators, and three causal pathways for ToM activation, with epistemic accuracy as the primary outcome. It offers a resource-rational framework for AI social reasoning, validated through simulation and human-machine studies.

arxiv arXiv cs.AI · 9d ago

Causal Framework for Auditing Synthetic Data Disclosures

A new empirical auditing framework detects and classifies synthetic data disclosures as either true or phantom. It distinguishes direct reproductions of user data from incidental generation without model access or training, using only synthetic output and a held-out control set. The method provides tighter privacy leakage bounds than prior approaches and requires significantly fewer computational resources.

arxiv arXiv cs.AI · 9d ago

Low Frame Rate Degradation in Neural Audio Codecs

A quality cliff at 6.25 Hz in neural audio codecs is caused by insufficient training token exposure due to fixed clip duration. Correcting this training configuration enables smooth WER degradation down to 3.1 Hz and 1.6 Hz, indicating low frame rate efficiency is more achievable than previously thought.

arxiv arXiv cs.AI · 9d ago

Textual Reviews Have Limited Impact in Recommendation Models

A study finds that while textual review signals can be fused with collaborative data, their marginal contribution remains limited compared to collaborative signals in matrix factorization models. Adaptive fusion and cross-attention mechanisms improve representation flexibility, but do not significantly boost performance across datasets.

arxiv arXiv cs.AI · 9d ago

AI research documentation improves over decade

Analysis of 56,800 AI conference papers shows documentation practices improved from 2014 to 2024. Papers sharing both code and data increased from 11% to 64%, and estimated reproducibility rose from 28% to 64%. These improvements predate formal reproducibility checklists, indicating a broader shift toward open science.

arxiv arXiv cs.AI · 9d ago

Agentic LLM Framework for HTS Code Classification

A consensus-based agentic large language model framework is proposed for accurate 10-digit Harmonized Tariff Schedule code classification in Canadian maritime logistics. Evaluated on 3,300 expert-labeled product records, the framework shows that fine-grained HTS classification remains challenging for advanced LLMs, highlighting the need for evidence-grounded, uncertainty-aware, and human-in-the-loop workflows.

arxiv arXiv cs.AI · 9d ago

AI-Enabled Progress in Stable Menus of Public Goods

Experiments on EC 2025's 'Stable Menus of Public Goods' show that human-intuition prompts improve LLM performance and multi-turn interactions enhance ambitious steps. However, when compared to a first-year PhD student using an unpublished manuscript, the LLM is found to be slightly less effective.

arxiv arXiv cs.AI · 9d ago

PACT: Small Language Model Deliberation for Reactive Reinforcement Learning

PACT combines a reactive RL policy with a 2B-parameter Small Language Model to generate and validate action plans. The SLM plan is executed directly if verified as safe, feasible, and complete, bypassing the RL policy. PACT outperforms baselines on three increasingly difficult FrozenLake environments.

arxiv arXiv cs.AI · 9d ago

ActiveSAM: Fast and Accurate Open-Vocabulary Segmentation

ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness under image corruption.

arxiv arXiv cs.AI · 9d ago

Bayesian Audits Reveal Inconsistent AI Evaluation Timelines

Public AI evaluation archives show that a single terminal result can arise from two distinct pre-terminal histories, with estimated times to reach 95% of performance ceilings at 23.03 or 75.13. A candidate selection-aware frontier model fails synthetic recovery and uncertainty calibration, and is rejected by fixed audit gates. An archive-and-adjudication protocol verifies timing boundaries and falsifies unsupported frontier claims.

arxiv arXiv cs.AI · 9d ago

TuneJury: Open Metric for Music Generation Preference Alignment

TuneJury is an open, instance-level pairwise reward model that predicts music preference scores from text prompts and audio clips. It is trained on diverse human-preference data and demonstrates strong generalization, with anchor calibration enabling efficient post-hoc alignment for music generation systems.

arxiv arXiv cs.AI · 9d ago

TokenPilot: Cache-Efficient Context Management for LLM Agents

TokenPilot reduces inference costs by 61% to 87% in both isolated and continuous modes, outperforming prior systems in cost efficiency while maintaining competitive performance. It uses ingestion-aware compaction and lifecycle-aware eviction to preserve prompt cache continuity and minimize token footprint without introducing prefix mismatches.