AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
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.
Continual Learning via Sparse Memory Finetuning
Meta AI researchers address catastrophic forgetting in language models through sparse memory finetuning, updating only memory slots most activated by new knowledge while achieving 89% less performance degradation than standard finetuning.
When Models Manipulate Manifolds
Anthropic researchers investigate how Claude 3.5 Haiku learns to predict line breaks in fixed-width text, revealing geometric representations analogous to biological place cells and boundary cells in biological brains.
Bayesian Influence Functions for Hessian-Free Data Attribution
Classical influence functions struggle with deep neural networks due to non-invertible Hessians and high-dimensional parameter spaces. This work introduces the local Bayesian influence function (BIF), which replaces Hessian inversion with loss landscape statistics estimated via stochastic-gradient MCMC sampling.
Reasoning with Sampling
Base language models achieve reasoning performance matching or exceeding RL-posttraining through inference-time power distribution sampling, using MCMC techniques that require no training, datasets, or verifiers.
Lookahead Routing for LLMs
Lookahead is a response-aware LLM routing framework that predicts latent representations of potential model outputs to enable more informed routing decisions without full inference.
Ring-1T
Ring-1T is the first open-source thinking model with 1 trillion parameters (~50B active per token), achieving breakthrough results through three innovations for trillion-scale RL training.
Demystifying RL in Agentic Reasoning
This paper studies what actually works when using RL to improve tool-using LLM agents, across three axes: data, algorithm, and reasoning mode. The team contributes a real end-to-end SFT dataset, a diverse RL set, and a compact 4B agent that beats larger models on agentic benchmarks.
Elastic-Cache
A training-free, architecture-agnostic way to make diffusion LLM decoding fast by updating KV caches only when and where it matters. Instead of recomputing QKV for all tokens at every denoising step, Elastic-Cache watches attention drift on the most-attended tokens and refreshes only deeper layers while reusing shallow and off-window caches. Results: large speedups with minimal or no accuracy loss across math, code, and multimodal tasks.
Hybrid Reinforcement
HERO (Hybrid Ensemble Reward Optimization) is a reinforcement learning framework that combines binary verifier feedback with continuous reward-model signals to improve LLM reasoning. By using stratified normalization and variance-aware weighting, HERO balances correctness and nuance, outperforming verifier-only and RM-only methods on diverse math reasoning benchmarks and enhancing performance on both verifiable and ambiguous tasks.
Holistic Agent Leaderboard
The Holistic Agent Leaderboard (HAL) introduces a standardized framework for large-scale, reproducible AI agent evaluation across 9 models and 9 benchmarks, spanning coding, web navigation, science, and customer service. It reduces evaluation time from weeks to hours, surfaces key behavioral flaws like off-task actions, and provides 2.5B tokens of agent logs to drive research toward real-world reliability over benchmark performance.
Agentic Context Engineering (ACE)
Presents a modular context-engineering framework that grows and refines an LLM’s working context like a playbook, not a terse prompt. ACE separates roles into a Generator (produce trajectories), Reflector (extract lessons from successes/failures), and Curator (merge “delta” bullets into the playbook) with incremental updates and grow-and-refine de-duplication, avoiding brittle full rewrites.
Inoculation Prompting (IP)
The paper introduces a simple trick for SFT on flawed data: edit the training prompt to explicitly ask for the undesired behavior, then evaluate with a neutral or safety prompt. Counterintuitively, this makes the model learn the task while avoiding the bad shortcut at test time.
Reasoning over Longer Horizons via RL
The authors show that you can scale long-horizon reasoning without step labels or heavy scaffolding. They synthesize long problems by chaining easy ones, then train with outcome-only rewards under a length curriculum. The result: large gains on both in-domain chains and harder out-of-domain math and long-context tasks.
The Markovian Thinker
A new RL thinking environment that keeps an LLM’s effective state constant by chunking long chains of thought and carrying over only a short textual state between chunks. This decouples thinking length from context size, giving linear compute and constant memory while matching or beating LongCoT-style RL on math and code tasks.
Abstract Reasoning Composition
UC San Diego and UMD propose ArcMemo, a test-time memory framework that distills reusable concepts from solution traces, stores them in natural language, and retrieves a relevant subset on future queries. Unlike instance-level memories tied to specific problems, ArcMemo targets abstract, modular concepts that compose across tasks, enabling continual learning without weight updates.
Artificial Hippocampus Networks
Artificial Hippocampus Networks add a fixed-size recurrent memory to sliding-window Transformers, compressing evicted KV into RNN-like states (Mamba2/DN/GDN) trained via self-distillation for long-context efficiency with constant cache and near-linear compute. On LV-Eval 128k, Qwen2.5-3B + AHN (+0.4% params) cuts FLOPs 40.5% and cache 74% while raising average from 4.41 to 5.88, though exact-recall NIAH tasks still favor full attention.
The Era of Real-World Human Interaction
This work presents a post-training recipe that learns directly from real user conversations instead of static annotator labels. RLHI combines user-guided rewrites (using follow-ups as corrections) with persona-based rewards (ranking sampled candidates via a persona-conditioned reward model). Trained on WildChat conversations, it shows strong improvements in personalization, instruction following, and even transfers to reasoning tasks.
DeepSearch
DeepSearch integrates Monte Carlo Tree Search directly into RL with verifiable rewards, but during training rather than only at inference. The result is broader exploration, better credit assignment, and higher sample efficiency on math reasoning vs strong 1.5B baselines.
PrompCoT 2.0
PromptCoT 2.0 introduces an EM-based loop for synthesizing harder and more diverse reasoning prompts, replacing manual heuristics from PromptCoT 1.0. It enables both self-play and SFT training regimes, achieving new SOTA on reasoning benchmarks like AIME, HMMT, LiveCodeBench, and Codeforces, showing prompt synthesis as a new scaling axis for LLM reasoning.
ARE
Metal SuperIntelligence Labs presents a research platform and benchmark for building and stress-testing agent systems in realistic, time-driven environments. The paper introduces a modular simulator (ARE) and a mobile-style benchmark (Gaia2) that emphasize asynchronous events, verification of write actions, and multi-agent coordination in noisy, dynamic settings.
Code World Model
Meta FAIR releases CWM, a 32B open-weights coder trained to model code execution and to act inside containers. It mid-trains on Python interpreter traces and agentic Docker trajectories, then upgrades with multi-turn RL across SWE, coding, and math. CWM is both a strong coder and a testbed for world-model-style reasoning in software environments.
GDPval
GDPval is a new benchmark of 1,320 real-world tasks across 44 occupations in 9 major GDP sectors, graded by industry experts with a 220-task gold set. It shows frontier models improve roughly linearly and are nearing expert parity, with Claude Opus 4.1 preferred or tied 47.6% of the time, while GPT-5 leads in accuracy. Model-plus-human workflows can reduce time and cost, and adding reasoning effort and prompt scaffolding further raises scores, with an open gold set and automated grader available for researchers.
Discovery of Unstable Singularities
The authors present a playbook for finding unstable finite-time singularities in fluid PDEs, uncovering new self-similar blow-up solutions in three canonical systems and training neural solvers to near machine precision, which enables downstream computer-assisted proofs.