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.
DeepDive
DeepDive builds a stronger web-browsing deep search agent by pairing two ingredients: automatically synthesized, hard-to-find questions from knowledge graphs and end-to-end multi-turn RL that teaches the model how to reason, search, and stop. On BrowseComp, the 32B model reaches 14.8% and beats prior open agents, with clear gains from RL over SFT.
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.
A Survey on Retrieval and Structuring Augmented Generation with LLMs
This survey reviews Retrieval and Structuring (RAS) Augmented Generation, which combines external retrieval and structured knowledge to mitigate LLM issues like hallucinations and outdated knowledge. It covers retrieval methods, structuring techniques, integration strategies, and highlights challenges in efficiency, structure quality, and multimodal or cross-lingual extensions.
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.
ACE-RL
A reinforcement-learning framework that replaces coarse, preference-pair rewards with instruction-specific, verifiable checklists. ACE-RL turns each long-form task into a set of explicit and implicit constraints, scores a model’s output by how well it satisfies them, and mixes this with a length-control reward during GRPO training. The result is stronger, more controllable long-form writing across domains and styles.
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.
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.
LiveMCP-101
LiveMCP-101 is a new benchmark of 101 real-world queries designed to test MCP-enabled agents on multi-step tasks requiring tool use across search, file ops, math, and data analysis. Results show leading LLMs succeed less than 60%, revealing key weaknesses in tool orchestration and offering insights for advancing autonomous AI systems.
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.
Disentangling the Factors of Convergence between Brains and Computer Vision Models
Large self-supervised ViTs trained on natural images develop brain-like internal representations. This paper teases apart what drives that convergence by varying model size, training amount, and image type in DINOv3, then comparing model activations to human fMRI (space) and MEG (time) with three metrics: overall linear predictability (encoding), cortical topography (spatial), and temporal alignment (temporal). Result: all three factors matter, and alignment unfolds in a consistent order from early sensory to higher associative cortex.
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.
On the Theoretical Limitations of Embedding-based Retrieval
Single-vector dense retrievers cannot realize all possible top-k relevance combinations once queries demand sufficiently many “mix-and-match” document sets. The paper ties this failure to the sign-rank of the relevance matrix, proves lower and upper bounds on the embedding dimension needed, and then stress-tests models with a simple but adversarially combinatorial dataset (LIMIT).
Self-Evolving Agents
This survey reviews techniques for building self-evolving AI agents that continuously adapt through feedback loops, bridging static foundation models with lifelong adaptability. It introduces a unified framework, covers domain-specific strategies, and discusses evaluation, safety, and ethics in advancing autonomous agentic systems.
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.
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.
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.
Assessing Language Models on Unsolved Questions
The paper introduces a new evaluation paradigm that tests models on real unsolved questions from the wild, rather than on fixed-answer exams. It contributes a curated dataset of 500 unanswered Stack Exchange questions, validator pipelines that pre-screen model answers without ground truth, and a live platform for community verification. The approach targets both difficulty and real-world relevance.
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.
Measuring the Environmental Impact of Delivering AI at Google Scale
Google presents first‑party, production measurements of AI serving’s environmental impact for Gemini Apps. Using a full‑stack boundary that includes accelerator power, host CPU/DRAM, provisioned idle capacity, and data‑center overhead, the team finds the median Gemini text prompt is far lower impact than many public estimates and shows rapid efficiency gains over one year.