AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
K2-Think
A 32B-parameter system built on Qwen2.5 that rivals or beats far larger models on hard math by combining long CoT SFT, RL with verifiable rewards, lightweight test-time scaffolding, and inference optimization.
Is In-Context Learning Learning?
This large study argues yes in a formal sense, then shows where it works and where it breaks. The author frames ICL within PAC learning, then runs a big empirical sweep to separate learning from memorization, prompt wording, and distribution shifts.
Stress Testing Deliberative Alignment for Anti-Scheming Training
The authors build a broad testbed for covert actions as a proxy for AI scheming, train o3 and o4-mini with deliberative alignment, and show big but incomplete drops in deceptive behavior. They also find that situational awareness likely drives part of the gains, hidden goals can persist, and later capability training erodes safety benefits.
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.
Emergent Hierarchical Reasoning
The paper argues that RL improves LLM reasoning via an emergent two-phase hierarchy: first the model firms up low-level execution, then progress hinges on exploring high-level planning. Building on this, the authors propose HICRA, which boosts credit on strategic planning tokens, and show consistent gains over GRPO. They also propose semantic entropy as a better exploration signal than token-level entropy.
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.
Talk Isn’t Always Cheap
Multi-agent debate does not always help. Across three reasoning benchmarks and heterogeneous agent pools, debate often lowers accuracy, with stronger models sometimes swayed into worse answers by weaker peers. The authors argue that current alignment makes agents too agreeable, so they adopt persuasive but wrong reasoning instead of challenging it.
A Survey of RL for Large Reasoning Models
This survey reviews how reinforcement learning is driving advances in large reasoning models (LRMs), enabling stronger performance on complex tasks like math and coding. It highlights scaling challenges in computation, algorithms, data, and infrastructure, while mapping future directions toward Artificial Superintelligence (ASI).
Why Language Models Hallucinate
The paper argues that hallucinations are not mysterious glitches but the predictable result of how LLMs are trained and evaluated. Pretraining creates statistical pressure to make errors, and post-training benchmarks often reward confident guessing over honest uncertainty. The fix is to realign mainstream evaluations to stop penalizing abstentions.
Universal Deep Research
Proposes a general, model-agnostic deep-research agent that lets users “bring your own model and strategy.” Instead of a fixed pipeline, UDR compiles natural-language research strategies into executable code, runs them in a sandbox, and emits structured progress notifications before returning a final report.
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.
Implicit Reasoning in LLMs
This survey defines implicit reasoning as multi-step problem solving that happens inside a model’s latent states without printing intermediate steps. It organizes the field by execution paradigm rather than representation format, and reviews evidence, evaluation, and open challenges.
Hermes 4
Hermes 4 introduces a family of hybrid reasoning models that integrate structured multi-turn reasoning with broad instruction-following. The report details data and training challenges, evaluates performance across reasoning, coding, and alignment tasks, and publicly releases all model weights.
Anemoi Agent
Anemoi replaces purely centralized, context-stuffed coordination with an A2A communication server (MCP) that lets agents talk directly, monitor progress, refine plans, and reach consensus. On GAIA, it holds up even with a small planner model and reduces redundant context passing for better cost and scalability.
Deep Think with Confidence
A lightweight test-time method that uses model-intrinsic confidence to prune weak reasoning paths, improving both accuracy and token efficiency for self-consistency ensembles. Works in offline and online modes without extra training or hyperparameter tuning.
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.
Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains
A test-time reasoning framework that replaces a single linear chain with multiple parallel, entity-grounded chains over medical knowledge graphs. MIRAGE decomposes a query into sub-questions, runs adaptive graph retrieval in Anchor and Bridge modes, then reconciles answers via cross-chain verification, yielding higher accuracy and clearer provenance than linear ToT or web-centric agentic RAG.
Synthetic Dataset Generation for RAG Evaluation with Multi-Agent Systems
The paper proposes a modular, three-agent pipeline that auto-generates synthetic QA datasets for evaluating RAG systems while enforcing privacy. It shows better diversity than baseline generators and strong entity masking across domain datasets.
Has GPT-5 Achieved Spatial Intelligence?
This report introduces a unified view of spatial intelligence (SI) for multimodal models and evaluates GPT‑5 and strong baselines across eight fresh SI benchmarks. GPT‑5 leads overall but is still short of human skill, especially on mentally reconstructing shapes, changing viewpoints, and deformation/assembly tasks.
Full-Stack Fine-Tuning for the Q Programming Language
Presents an open-source blueprint for adapting large language models to niche programming domains, with Q (used in quantitative finance) as the test case. The team builds a benchmark, curates data, and trains Qwen-2.5 models with pretraining, supervised fine-tuning, and reinforcement learning. Their largest model surpasses Claude Opus-4 by nearly 30% on Q-LeetCode tasks, and even the smallest model beats GPT-4.1.
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.
Open Foundations for Compute-Use Agents
This paper introduces an open-source framework for computer-use agents, featuring a large-scale dataset (AgentNet), annotation infrastructure, and reflective reasoning pipelines. Its 32B model sets a new SOTA on OSWorld-Verified with 34.8% success, surpassing OpenAI’s GPT-4o, and all resources are released to advance open CUA research.
Capabilities of GPT-5 on Multimodal Medical Reasoning
A controlled, zero‑shot CoT evaluation positions GPT‑5 as a generalist medical reasoner across text and image inputs. Using standardized prompts and splits, GPT‑5 consistently beats GPT‑4o and smaller GPT‑5 variants, with especially large gains on multimodal expert‑level QA.
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.