LLM agents rely on long-term interaction histories to tailor their responses to individual users. However, historical personalization signals may become stale or misaligned with expectations implied by the current context, leading agents to follow outdated preferences during reasoning. We refer to this failure mode as personalization inertia, which shifts the agent’s behavior away from the user’s current intent. Existing methods mainly optimize final responses or memory retrieval, leaving such failures underexplored. In this paper, we propose Personalization Inertia VerificatiOn and RegulaTion (Pivot), a framework for mitigating personalization inertia in long-term interactions with agents. Concretely, we construct a trajectory-independent evidence condition as a diagnostic reference, model prefix-evidence consistency to assess compatibility with the current personalization evidence, and learn a rollback policy to determine whether and where to intervene. The selected intervention triggers local repair, preserving the reliable prefix while regenerating only the potentially inertia-affected suffix under evidence-guided constraints. Experiments on benchmark datasets demonstrate consistent gains in personalization accuracy and trajectory faithfulness with targeted intervention overhead.

Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, raw retrieved content often suffers from low signal-to-noise ratio, where answer-supportive information is diluted by irrelevant material, and error accumulation, where incomplete or misleading content degrades multi-hop reasoning. To address these challenges, we propose EviNoteRAG, a framework that enhances RAG through quality-augmented evidence notes. The model is trained to generate Supportive-Evidence Notes (SENs), concise abstractions that preserve only answer-critical information and explicitly highlight key and uncertain content to improve reasoning accuracy. To further ensure note quality, we introduce an entailment-based Evidence Quality Reward (EQR), which incentivizes SENs to be logically sufficient for deriving the correct answer. Experiments on both in-domain and out-of-domain benchmarks demonstrate that EviNoteRAG achieves state-of-the-art performance, with relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), while also improving training stability and efficiency.

Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended histories. However, existing agent memory systems are fundamentally constrained by text-context budgets: storing or revisiting raw trajectories is prohibitively token-expensive, while summarization and text-only retrieval trade token savings for information loss and fragmented evidence. To address this limitation, we propose Optical Context Retrieval Memory (OCR-Memory), a memory framework that leverages the visual modality as a high-density representation of agent experience, enabling retention of arbitrarily long histories with minimal prompt overhead at retrieval time. Specifically, OCR-Memory renders historical trajectories into images annotated with unique visual identifiers. OCR-Memory retrieves stored experience via a locate-and-transcribe paradigm that selects relevant regions through visual anchors and retrieves the corresponding verbatim text, avoiding free-form generation and reducing hallucination. Experiments on long-horizon agent benchmarks show consistent gains under strict context limits, demonstrating that optical encoding increases effective memory capacity while preserving faithful evidence recovery.

Large Language Models (LLMs) are increasingly adopted as conversational assistants in genomics, where they are mainly used to reason over biological knowledge, annotations, and analysis outputs through natural language interfaces. However, existing benchmarks either focus on specialized DNA models trained for sequence prediction or evaluate biological knowledge using text-only questions, leaving the behavior of general-purpose LLMs when directly exposed to raw genome sequences underexplored. We introduce GenomeQA, a benchmark designed to provide a controlled evaluation setting for general-purpose LLMs on sequence-based genome inference tasks. GenomeQA comprises 5,200 samples drawn from multiple biological databases, with sequence lengths ranging from 6 to 1,000 base pairs (bp), spanning six task families: Enhancer and Promoter Identification, Splice Site Identification, Taxonomic Classification, Histone Mark Prediction, Transcription Factor Binding Site Prediction, and TF Motif Prediction. Across six frontier LLMs, we find that models often outperform random baselines, particularly on tasks driven by local sequence cues such as GC content and short motifs, while performance degrades on tasks that require more indirect or multi-step inference over sequence patterns. GenomeQA establishes a diagnostic benchmark for studying and improving the use of general-purpose LLMs on raw genomic sequences.