The week of 3 August holds 2,008 ingested papers, against 1,385 the week before. Three neighbouring topics rise together: information retrieval and search behaviour (73 papers over four weeks against 26 over the four before), recommender systems (59 against 27) and personal information management (42 against 26).
Behind those three labels lies one movement: search and recommendation systems are being rewritten around agents built on language models. This week's titles bear less on what those agents promise than on how they are assessed. What their representations are worth, what happens when their own history misleads them, how to trace an answer back to its source.
Worth reading: PoolBench: A Benchmark for Pooling Strategies in Concept Representation Evaluation for Decoder-Only LLMs When History Lies: Evaluating and Improving Tool Use under Misleading Multi-Turn Histories Universal Pathologies, Conditional Consequences: A Triple-Robustness Analysis of RAG for Multi-Hop Traceability A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing