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The paper introduces PRO-LONG, which uses a programmatic, lossless memory system for AI agents, storing all past actions in a structured text log. This approach significantly improves long-horizon reasoning and performance on ARC-AGI-3 games while using fewer tokens than stronger specialized systems.
LongMedBench is a new benchmark for evaluating LLM-based medical agents on long-horizon clinical decision-making using real EHR data from MIMIC-IV. It includes 335 patients with multiple visits and proposes evaluation suites for fact-based QA, temporal reasoning, and long-horizon decision-making.
This ICML paper introduces recursive models that recursively invoke themselves to solve subtasks in isolated contexts, proving they can surpass context-bounded autoregressive models for long-horizon reasoning. Experiments on SAT solving and Go game-tree search show improved accuracy with small active contexts.
Sakana AI launches its first commercial product, Sakana Marlin, an autonomous research assistant that completes strategy work in hours by generating structured slides and detailed reports.
This paper presents SPADER, a reinforcement learning framework for multi-answer QA that uses step-wise peer advantage for credit assignment and diversity-aware exploration rewards to improve recall of long-tail entities, achieving better performance on several benchmarks.
This paper proposes SAM, a state-adaptive memory framework that dynamically manages interaction histories for long-horizon agentic reasoning, enabling intent-driven recall without retraining the backbone model. It outperforms strong baselines across multiple benchmarks like BrowseComp and HLE.
The paper proposes the Map-then-Act Paradigm (MAP), a plug-and-play framework that shifts environmental understanding before execution in interactive LLM agents, achieving consistent gains across benchmarks and enabling frontier models to surpass near-zero baseline performance in 22 of 25 game environments.
HAGE introduces a weighted multi-relational memory framework that enables query-conditioned traversal over unified relational memory graphs, improving long-horizon reasoning accuracy through adaptive memory retrieval and reinforcement learning-based optimization.