AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a 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.
mem-agent
mem-agent is a 4B-parameter LLM trained with GSPO reinforcement learning to develop persistent memory using a scaffold of Python tools and markdown files. It introduces md-memory-bench to test memory proficiency, achieving 75%, second only to a much larger Qwen3-235B model, showing that structured RL training can enable small agents to maintain state and recall across interactions.
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.
Training Agents Inside of Scalable World Models
A scalable imagination-RL recipe that learns a fast, accurate Minecraft simulator and trains a controllable agent entirely offline. The world model supports real-time interactive rollouts on a single GPU and enables the first purely offline “get diamonds” result from raw pixels and low-level mouse and keyboard.
DeepSeek-V3.2-Exp
DeepSeek adds a fine-grained sparse attention mechanism (DeepSeek Sparse Attention, DSA) to the V3.1 “Terminus” backbone and shows large cost reductions on 128K context without notable quality loss. Model and inference code are released.
Accelerating Diffusion LLMs
A lightweight, learned policy speeds up diffusion-based LLM decoding by deciding which tokens are already “final” and when to stop generation. The authors train a tiny MLP filter on token confidence signals and add an End-of-Text Prediction that halts decoding as soon as [EoT] is reliably produced. On LLaDA-8B-Instruct, this reaches large throughput gains with minimal or no accuracy loss.
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.
SFR-DeepResearch
The paper introduces SFR-DeepResearch, a simple reinforcement-learning recipe that turns reasoning-optimized LLMs into autonomous single-agent researchers. The agent uses only three tools (search, static page browse, Python), manages its own context, and is trained end-to-end on synthetic short-form and long-form tasks with a length-normalized REINFORCE objective. Results show strong gains on FRAMES, GAIA, and Humanity’s Last Exam.
Rethinking RAG-based Decoding
REFRAG replaces most retrieved tokens with precomputed chunk embeddings at decode time, then selectively expands only the few chunks that matter. This exploits block-diagonal attention in RAG prompts to cut latency and memory while preserving accuracy across RAG, multi-turn dialog, and long-doc summarization.
ParaThinker
This paper argues that today’s “think longer” strategies trap LLMs in a single line of thought. They propose ParaThinker, which trains models to generate several independent reasoning paths in parallel and then fuse them into one answer. Across math benchmarks, this width-scaling lifts accuracy while adding only a small latency cost.
AgentGym-RL
A modular framework for training LLM agents directly via reinforcement learning across realistic environments, plus a simple schedule, ScalingInter-RL, that lengthens interaction horizons over training to improve stability and performance. Results show a 7B open model can rival or beat larger proprietary systems on web navigation, deep search, games, embodied, and science tasks.
rStar2-Agent
rStar2-Agent is a 14B math-reasoning model trained with agentic RL that learns to think smarter by using a Python tool environment, not just longer CoT. It introduces GRPO-RoC, a rollout strategy that filters noisy successful traces, plus infrastructure for massive, low-latency tool execution. In one week and 510 RL steps on 64 MI300X GPUs, the model reaches frontier-level AIME while producing shorter solutions and showing transfer beyond math.
Fine-tuning LLM Agents without Fine-tuning LLMs
A memory‑based learning framework that lets deep‑research agents adapt online without updating model weights. The agent is cast as a memory‑augmented MDP with case‑based reasoning, implemented in a planner–executor loop over MCP tools. It sets top validation results on GAIA and delivers strong scores on DeepResearcher, SimpleQA, and HLE.
Jet-Nemotron
A hybrid-architecture LM family built by adapting after pretraining. Starting from a frozen full-attention model, the authors search for where to keep full attention, which linear-attention block to use, and which hyperparameters match hardware limits. The result, Jet-Nemotron-2B/4B, matches or surpasses popular full-attention baselines while massively increasing throughput on long contexts.
Memory-R1
A framework that teaches LLM agents to decide what to remember and how to use it. Two RL-fine-tuned components work together: a Memory Manager that learns CRUD-style operations on an external store and an Answer Agent that filters retrieved memories via “memory distillation” before answering. Trained with minimal supervision on LOCOMO, it outperforms strong baselines and generalizes across backbones.
Retrieval-Augmented Reasoning with Lean Language Models
A domain-tuned pipeline that fuses RAG and reasoning into a single small-footprint model. The team distills reasoning traces from a frontier model into Qwen2.5 variants, uses summarization to keep context small, and shows that a 32B local model approaches frontier accuracy on an NHS A‑to‑Z clinical QA task.
M3-Agent
A framework for agents that watch and listen to long videos, build entity-centric memories, and use multi-turn reasoning to answer questions. M3-Agent stores both episodic details and distilled semantic knowledge in a multimodal memory graph, then learns a retrieval-reasoning policy with RL. The authors also introduce M3-Bench, a long-video QA benchmark with robot-view and web videos. Results show consistent gains over strong prompting baselines.
TRImodal Brain Encoder
A tri‑modal, multi‑subject, nonlinear encoder that fuses text, audio, and video features with a transformer to predict time‑varying fMRI responses to natural movies. It took 1st place in the Algonauts 2025 brain encoding competition and shows the strongest gains in the high‑level associative cortex.
OdysseyBench
A new benchmark and data‑generation pipeline to test agents on realistic, multi‑day office tasks across Word, Excel, PDF, Email, and Calendar. It introduces OdysseyBench (two splits) and HOMERAGENTS (an automated multi‑agent generator), with evaluations showing large gaps between human and model performance and clear benefits from semantically compressed memory.
Efficient Agents
This paper presents Efficient Agents, a new agent framework that achieves a strong efficiency-effectiveness balance in LLM-driven systems. The authors perform the first systematic study of agent design choices through the lens of economic efficiency, specifically, the cost-of-pass metric, which captures the expected monetary cost of solving a task. Their proposed agent retains 96.7% of OWL’s performance on the GAIA benchmark while cutting costs by 28.4%.
Agentic Web
This paper introduces the concept of the Agentic Web, a transformative vision of the internet where autonomous AI agents, powered by LLMs, act on behalf of users to plan, coordinate, and execute tasks. It proposes a structured framework for understanding this shift, situating it as a successor to the PC and Mobile Web eras. The Agentic Web is defined by a triplet of core dimensions, intelligence, interaction, and economics, and involves fundamental architectural and commercial transitions.
ReaGAN
This paper introduces ReaGAN, a graph learning framework that reconceptualizes each node in a graph as an autonomous agent capable of planning, reasoning, and acting via a frozen LLM. Instead of relying on static, layer-wise message passing, ReaGAN enables node-level autonomy, where each node independently decides whether to aggregate information from local neighbors, retrieve semantically similar but distant nodes, or take no action at all. This node-agent abstraction addresses two key challenges in graph learning: (1) handling varying informativeness of nodes and (2) combining local structure with global semantics.