AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Nemotron-Research-Tool-N1
Introduces Tool-N1, a family of tool-using LLMs trained using a rule-based reinforcement learning (R1-style RL) approach, without reliance on supervised reasoning trajectories. The key idea is to enable models to learn to invoke external tools correctly through binary feedback based on functional correctness and format adherence, rather than step-by-step imitation.
RL for Search-Efficient LLMs
Proposes a new RL-based framework (SEM) that explicitly teaches LLMs when to invoke search and when to rely on internal knowledge, aiming to reduce redundant tool use while maintaining answer accuracy. Key points:
CellVerse
Introduces a benchmark to evaluate LLMs on single-cell biology tasks by converting multi-omics data into natural language. While generalist LLMs like DeepSeek and GPT-4 families show some reasoning ability, none significantly outperform random guessing on key tasks like drug response prediction, exposing major gaps in biological understanding by current LLMs.
Llama-Nemotron
NVIDIA introduces the Llama-Nemotron model series, LN-Nano (8B), LN-Super (49B), and LN-Ultra (253B), a family of open, efficient, and high-performing reasoning models. These models rival or outperform DeepSeek-R1 on various benchmarks while offering significantly better inference throughput and memory efficiency. LN-Ultra is noted as the most "intelligent" open model by Artificial Analysis. A key innovation is a dynamic reasoning toggle ("detailed thinking on/off") that allows users to control reasoning behavior at inference time. Highlights:
Absolute Zero
Introduces an LLM training framework that eliminates the need for human-curated data. Key highlights:
Discuss-RAG
This paper introduces Discuss-RAG, a plug-and-play agent-based framework that enhances retrieval-augmented generation (RAG) for medical question answering by mimicking human-like clinical reasoning. Standard RAG systems rely on embedding-based retrieval and lack mechanisms to verify relevance or logical coherence, often leading to hallucinations or outdated answers. Discuss-RAG addresses these gaps via a modular agent setup that simulates multi-turn medical discussions and performs post-retrieval verification. Key ideas:
WebThinker
This paper introduces a reasoning agent framework that equips large reasoning models (LRMs) with autonomous web exploration and report writing abilities to overcome limitations of static internal knowledge. WebThinker integrates a Deep Web Explorer module and an Autonomous Think-Search-and-Draft strategy that lets models search the web, reason through tasks, and generate comprehensive outputs simultaneously. It also incorporates an RL-based training loop using online DPO to improve tool usage. The system supports two modes: complex problem solving and scientific report generation. Key points:
Reward Modeling as Reasoning
This work proposes a new class of reward models, called ReasRMs, that reformulate reward modeling as a reasoning task. The authors introduce RM-R1, a family of generative reward models that produce interpretable reasoning traces and rubrics during preference judgments. Instead of relying on scalar scores or shallow generation, RM-R1 models leverage structured reasoning and reinforcement learning to improve both interpretability and performance across benchmarks.
Phi-4-Mini-Reasoning
Microsoft released Phi-4-Mini-Reasoning to explore small reasoning language models for math. Highlights:
Building Production-Ready AI Agents with Scalable Long-Term Memory
This paper proposes a memory-centric architecture for LLM agents to maintain coherence across long conversations and sessions, solving the fixed-context window limitation. Main highlights:
UniversalRAG
UniversalRAG is a framework that overcomes the limitations of existing RAG systems confined to single modalities or corpora. It supports retrieval across modalities (text, image, video) and at multiple granularities (e.g., paragraph vs. document, clip vs. video). Contributions from the paper:
DeepSeek-Prover-V2
DeepSeek-Prover-V2 is an LLM (671B) that significantly advances formal theorem proving in Lean 4. The model is built through a novel cold-start training pipeline that combines informal chain-of-thought reasoning with formal subgoal decomposition, enhanced through reinforcement learning. It surpasses prior state-of-the-art on multiple theorem-proving benchmarks. Key highlights:
MiMo-7B
Xiaomi releases MiMo-7B, a new language model for reasoning tasks. MiMo-7B is explicitly designed for advanced reasoning across math and code. Highlights:
Advances and Challenges in Foundation Agents
A new survey frames intelligent agents with a modular, brain-inspired architecture that integrates ideas from cognitive science, neuroscience, and computational research. Key topics covered:
MAGI
MAGI is a multi-agent system designed to automate structured psychiatric interviews by operationalizing the MINI (Mini International Neuropsychiatric Interview) protocol. It involves 4 specialized agents: navigation, question generation, judgment, and diagnosis. Other highlights:
Does RL Incentivize Reasoning in LLMs Beyond the Base Model?
This paper revisits a key assumption in recent LLM development: that Reinforcement Learning with Verifiable Rewards (RLVR) helps models acquire genuinely new reasoning capabilities. By analyzing models across tasks (math, code, vision) using pass@k metrics (with large k), the authors find that RLVR improves sample efficiency but does not expand reasoning capacity beyond the base model.
UI-TARS
UI-TARS introduces a powerful, end-to-end native GUI agent that operates purely from visual screenshots, performing human-like keyboard and mouse interactions across platforms. Unlike existing modular agent frameworks that rely on prompt engineering and external scripts, UI-TARS integrates perception, action, reasoning, and memory directly into its architecture, achieving strong generalization and adaptability in dynamic real-world settings. Key contributions:
UXAgent
Introduces a novel framework, UXAgent, for simulating large-scale usability testing using LLM-driven agents. The system empowers UX researchers to test and iterate web design and study protocols before engaging real users. This is achieved through the orchestration of simulated agents with diverse personas interacting in real web environments, providing both behavioral and reasoning data. Key highlights:
General-Reasoner
General-Reasoner is a reinforcement learning approach that boosts LLM reasoning across diverse domains by using a 230K-question dataset and a model-based verifier trained to understand semantics beyond exact matches. It outperforms strong baselines like SimpleRL and Qwen2.5 on both general reasoning (MMLU-Pro, GPQA, SuperGPQA) and math tasks (MATH-500, GSM8K), showing over 10-point gains without sacrificing mathematical capability.
Tiny Reasoning Models
Tina is a family of 1.5B parameter reasoning models trained using LoRA-based reinforcement learning (RL) to achieve high reasoning accuracy at very low cost. It outperforms or matches full fine-tuned models on reasoning tasks like AIME and MATH with only ~$9 post-training cost, demonstrating that efficient reasoning can be instilled via minimal updates to a tiny model.
Scaling Reasoning in Diffusion LLMs via RL
Proposes d1, a two‑stage recipe that equips masked diffusion LLMs with strong step‑by‑step reasoning.
Enhancing Non-Reasoning Models with Reasoning Models
Researchers explore how to distill reasoning-intensive outputs (answers and explanations) from top-tier LLMs into more lightweight models that don’t explicitly reason step by step. By fine-tuning smaller models on the high-quality final answers (and optionally summarized thinking traces) from advanced reasoning models, they demonstrate consistent performance boosts across multiple benchmarks.
AgentA/B
AgentA/B is a fully automated A/B testing framework that replaces live human traffic with large-scale LLM-based agents. These agents simulate realistic, intention-driven user behaviors on actual web environments, enabling faster, cheaper, and risk-free UX evaluations — even on real websites like Amazon. Key Insights:
Reasoning Models Can Be Effective Without Thinking
This paper challenges the necessity of long chain-of-thought (CoT) reasoning in LLMs by introducing a simple prompting method called NoThinking, which bypasses explicit "thinking" steps. Surprisingly, NoThinking performs comparably to or better than traditional reasoning under comparable or even lower compute budgets, especially when paired with parallel decoding and best-of-N selection. Key Insights: