AgentFold
AgentFold introduces proactive context management for long-horizon web agents, addressing context saturation through dynamic “folding” operations that balance detail preservation with efficient compression. The 30B parameter model outperforms dramatically larger competitors while achieving state-of-the-art results on web browsing benchmarks.
Introspective Awareness
Anthropic research demonstrates that contemporary LLMs possess limited but functional introspective capabilities, the ability to recognize and accurately report on their own internal states. Using activation steering to inject known concepts into model activations, the study measures whether models can detect these manipulations through self-report, revealing that introspection remains highly unreliable and context-dependent.
Multi-Agent Evolve
Multi-Agent Evolve (MAE) enables LLMs to self-improve their reasoning capabilities without human-annotated data through a co-evolving multi-agent framework. Three interacting agents (Proposer, Solver, Judge) instantiated from a single LLM undergo reinforcement learning optimization together, creating a scalable self-improving system that extends beyond game-based environments to general reasoning domains.
SmolLM2
SmolLM2 demonstrates that strategic data curation beats scale through a 1.7B parameter model trained on 11 trillion tokens using iterative data mixing optimization. The data-centric approach introduces three specialized datasets (FineMath, Stack-Edu, SmolTalk) and dynamically refines composition across training stages, achieving superior performance over Qwen2.5-1.5B and Llama3.2-1B while enabling practical on-device deployment.
Global PIQA
Global PIQA extends physical commonsense reasoning evaluation to 100+ languages and cultural contexts, revealing how language models handle everyday practical scenarios across diverse linguistic communities. The benchmark goes beyond translation to include culturally-contextualized scenarios, uncovering significant performance variations that challenge assumptions about universal physical understanding in AI systems.
GAP
GAP introduces graph-based agent planning with parallel tool execution and reinforcement learning, enabling AI agents to coordinate multiple specialized capabilities simultaneously rather than sequentially. The framework significantly accelerates task completion and improves success rates on complex multi-step problems through optimized tool selection and execution ordering.
Stress-Testing Model Specs
This research examines how well large language models adhere to their stated behavioral guidelines by stress-testing AI constitutional specifications through value-tradeoff scenarios. Testing twelve frontier LLMs from major providers revealed over 70,000 cases of significant behavioral divergence, exposing logical inconsistencies, coverage gaps, and interpretive ambiguities in current specification frameworks.
Agent Data Protocol
Agent Data Protocol introduces a standardized format to unify fragmented agent training datasets across different tools and interfaces, enabling more efficient fine-tuning of LLM agents. By converting 13 existing datasets into this protocol and training on consolidated data, the work achieved ~20% performance improvements over baseline models while reaching state-of-the-art results on coding, browsing, and tool-use benchmarks. The protocol and datasets are publicly released to facilitate reproducible, scalable agent training across diverse domains.
Kimi Linear
Kimi Linear introduces a hybrid linear attention architecture combining Kimi Delta Attention (KDA) with periodic full attention layers at a 3:1 ratio, achieving superior performance over full attention while reducing KV cache by 75% and delivering 6× faster decoding at 1M context. KDA extends Gated DeltaNet with fine-grained channel-wise gating and specialized Diagonal-Plus-Low-Rank matrices, enabling more effective RNN memory management while maintaining hardware efficiency through optimized chunkwise algorithms that substantially reduce computation versus general DPLR formulations.
Precision-RL
Reinforcement learning fine-tuning of LLMs suffers from a critical numerical mismatch between training and inference engines, causing training instability and collapse. This work reveals that simply switching from BF16 to FP16 precision virtually eliminates this mismatch - achieving faster convergence, higher stability, and superior performance across diverse models, frameworks, and algorithms without any algorithmic changes or architectural modifications.
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