AI Research news and analysis on Web Pulse: 9743 curated articles tracking the latest AI Research developments, tools, and research, updated continuously from vetted sources.
Researchers introduced TheraJudge and TheraAgent, a framework that uses multi-agent systems and human-aligned evaluation to improve therapeutic quality in mental health support LLMs, achieving a +0.43 improvement in clin…
Researchers introduced AgRefactor, an LLM-based multi-agent workflow that refactors software into HLS-compatible code with a self-evolving memory system. On 9 of 11 challenging benchmarks, it outperformed state-of-the-ar…
A new study using computational modeling shows that artificial agents exhibit hysteresis in regulatory control, where the same uncertainty target requires different levels of adaptive gain depending on the agent's histor…
Researchers released a benchmark for Arabic-Russian scientific translation, including a 27,000-sentence parallel corpus and fine-tuned multilingual models. The Qwen2.5-7B model achieved BLEU 23.15, outperforming zero-sho…
Researchers introduced Contrastive Reflection, an iterative prompt-optimization framework for agentic information retrieval workflows, which uses error-anchored behavioral slices and contrastive examples to propose targe…
Researchers introduced OpenLife, a proof-of-concept system that uses LLM agents with persistent memory, tool use, and a budget-based metabolism to enable open-world artificial life. Running six agents for twelve weeks, t…
Researchers introduced a Bangla event detection benchmark with 9,979 annotated sentences across clean text, ASR transcripts, and corrupted text. They found encoder models perform better on clean text but degrade under no…
Researchers introduced Indi-RomCoM, a benchmark for evaluating large language models on Romanized code-mixed Indic-English instructions. Testing across seven tasks and four languages showed that LLMs consistently underpe…
Researchers introduced a framework using generative AI agents to automate black-box audits of personalization algorithms, deploying 1,120 agents on X after the 2024 U.S. election. They found that X's algorithmic feed amp…
Researchers introduced GradeSQL, a framework for training Outcome Reward Models (ORMs) to improve test-time verification in Text-to-SQL tasks. ORM-based selection outperformed execution-based Best-of-N and Majority Votin…
Researchers propose a reference-based evaluation protocol for prosody and rhythm in speech-to-speech AI agents, using 4000+ hours of dyadic English conversation to create matched reference regimes for metrics like F0 and…
A new study from arXiv investigates how AI can retrieve simulation models using natural language queries, finding that data representation, open-source embedding models, and reranking strategies significantly impact retr…
Researchers introduced AgentBound, a runtime governance framework that provides verifiable behavioral oversight for autonomous AI agents by evaluating actions through three independent authorities and generating cryptogr…
Researchers deployed an automated pipeline to optimize skill descriptions for an enterprise AI agent, achieving 79.2% F1 accuracy versus 79.4% for manual tuning while reducing engineering effort per skill from 120 to 3.8…
Researchers at arXiv trained GPT-2 models on 'impossible' languages and found that while grammatical sensitivity degrades gradually, generative production fails significantly, suggesting transmission failures explain why…
Researchers extend Frictive Policy Optimization (FPO) to handle perceptual asymmetry in dialogue, where participants have asymmetric partial information. Cross-corpora analysis and LLM probing on the HCRC MapTask show th…
Researchers introduced Explanation Quality Markers (EQMs), a set of sixty reasoning patterns scored by large language models, to measure judgment quality in natural-language explanations. In a pre-registered analysis of …
Researchers at arXiv identify a failure mode in large language models called deductive stereotyping, where models apply population-level statistics to individuals, producing biased inferences. They propose Fair-GCG, a re…
Researchers propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG) that identifies ungrounded content by comparing internal representations of a large language model with an…
Researchers introduced LoFa, a benchmark for evaluating large language models' robustness against logical fallacies, using a multi-agent pipeline and a multi-round debate framework. Experiments revealed varying vulnerabi…