AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Budget Aware Test-time Scaling
Researchers discover that simply expanding tool-call budgets without proper awareness fails to improve agent performance. They introduce BATS (Budget Aware Test-time Scaling), a framework that makes web search agents budget-aware, enabling more strategic resource allocation and pushing the cost-performance Pareto frontier.
DeepCode
DeepCode is a fully autonomous framework for synthesizing complete codebases from scientific papers despite LLM context limitations. It treats repository synthesis as a channel optimization problem, achieving state-of-the-art on PaperBench and outperforming commercial tools like Cursor and Claude Code.
ARTEMIS
Stanford researchers conducted the first head-to-head evaluation of AI agents against human cybersecurity professionals on a live enterprise network with approximately 8,000 hosts. Their multi-agent framework ARTEMIS placed second overall, discovering 9 valid vulnerabilities with 82% accuracy and outperforming 9 of 10 human testers at a fraction of the cost (18 dollars per hour vs 60 dollars per hour for professionals).
Towards a Science of Scaling Agent Systems
Researchers from Google present a controlled evaluation framework for agent systems, challenging the assumption that “more agents are all you need.” Across 180 configurations spanning three LLM families and four agentic benchmarks, the study establishes quantitative principles for when multi-agent coordination helps versus hurts performance.
Agentic AI Adaptation Survey
Researchers from UIUC, Stanford, Berkeley, and other institutions present the first comprehensive taxonomy of adaptation strategies for agentic AI systems. The survey organizes recent advances into a unified framework covering how agents and their tools can be modified to achieve higher task performance, improved reliability, and better generalization across diverse scenarios.
AI and Human Co-Improvement
Meta FAIR researchers Jason Weston and Jakob Foerster argue that fully autonomous self-improving AI is neither the fastest nor safest path to superintelligence. Instead, they advocate for co-improvement: building AI that collaborates with human researchers to conduct AI research together, from ideation to experimentation.
AI Agent Adoption Study
Harvard and Perplexity researchers present the first large-scale field study of AI agent adoption using hundreds of millions of anonymized interactions from Perplexity’s Comet browser. Productivity and Learning account for 57% of agentic queries, with digital technology workers (28% of adopters) and knowledge-intensive sectors leading adoption. Users in higher GDP countries with greater educational attainment are more likely to adopt agents, and over time, users shift from media and travel tasks toward more cognitively oriented topics.
ProAgent
ProAgent is the first end-to-end proactive LLM agent system that harnesses sensory contexts from AR glasses, smartphones, and edge servers to deliver assistance without explicit user instructions. Unlike reactive agents that wait for commands, ProAgent continuously senses the environment with on-demand tiered perception and achieves up to 33.4% higher proactive prediction accuracy, 16.8% higher tool-calling F1 score, and 38.9% improved user satisfaction over baselines.
DeepSeek-V3.2
DeepSeek releases V3.2, an open model that matches GPT-5 on reasoning benchmarks while introducing significant architectural and training innovations. The high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and achieves gold-medal performance in both the 2025 IMO and IOI competitions.
SUSVIBES: Is Vibe Coding Safe?
Researchers introduce SUSVIBES, a benchmark of 200 real-world software engineering tasks to evaluate the security of code generated by LLM agents through “vibe coding” - the minimal-supervision programming paradigm. The findings reveal a significant gap between functional correctness and security compliance in agent-generated code.
Evolving Multi-Agent Orchestration
OpenBMB researchers propose a “puppeteer-style” paradigm for multi-agent LLM collaboration, where a centralized orchestrator dynamically directs agents based on evolving task states. Trained via reinforcement learning, the system achieves superior performance with reduced computational costs across math, knowledge, and software development tasks.
FINDER and DEFT
OPPO AI introduces FINDER, a fine-grained benchmark with 100 expert-curated research tasks and 419 structured checklist items for evaluating deep research agents, along with DEFT, the first failure taxonomy categorizing 14 failure modes across reasoning, retrieval, and generation dimensions.
STRATUS: Autonomous Cloud Reliability
Researchers from UIUC, IBM Research, and Tsinghua present STRATUS, an LLM-based multi-agent system for autonomous Site Reliability Engineering (SRE) of cloud services. The system handles failure detection, localization, root-cause analysis, and mitigation without human intervention, outperforming state-of-the-art SRE agents by at least 1.5x on benchmark suites.
INTELLECT-3
INTELLECT-3 is a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning, achieving state-of-the-art performance for its size across math, code, science, and reasoning benchmarks. Built on top of GLM-4.5-Air base, it outperforms many larger frontier models, including DeepSeek R1-0528, and matches GLM-4.6 (which has over 3x the parameters) on key benchmarks.
LatentMAS
LatentMAS introduces a framework enabling language model agents to collaborate directly within a continuous latent space rather than relying on text-based communication. By using last-layer hidden embeddings and a shared latent working memory, agents preserve and transfer internal representations without information loss from text serialization.
OmniScientist
OmniScientist presents an end-to-end framework for building AI scientists capable of autonomously conducting research across the entire scientific lifecycle - from literature review and ideation to experimentation, writing, and peer review. The system establishes a collaborative ecosystem where human and AI scientists co-evolve within a shared scientific environment.
InfCode
InfCode introduces an adversarial framework for software bug fixing that treats test generation and patch creation as mutually reinforcing processes. Rather than generating patches that simply pass existing tests, the system creates challenging test cases designed to expose patch weaknesses, then iteratively refines patches to handle these adversarial tests.
Multi-Agent Collaboration for Multimodal LLMs
Microsoft and USC researchers introduce a framework where vision models serve as “eyes” for language models through multi-agent collaboration, enabling modular upgrades without retraining expensive joint vision-language architectures. Specialized vision agents analyze images and communicate findings to language agents through natural language, achieving competitive results on MMMU, MMMU-Pro, and video understanding benchmarks while maintaining full flexibility to swap in improved components independently.
GPT-5 for Science Acceleration
OpenAI and collaborators present early case studies demonstrating GPT-5’s capabilities in accelerating scientific research across mathematics, physics, biology, computer science, astronomy, and materials science. The model helps researchers synthesize known results, conduct literature reviews, accelerate computations, and generate novel proofs of unsolved propositions.
DR Tulu
DR Tulu-8B is the first open model directly trained for long-form deep research using Reinforcement Learning with Evolving Rubrics (RLER). Unlike existing models trained on short-form QA tasks, DR Tulu learns to produce comprehensive, well-attributed research reports by training with rubrics that co-evolve with the model and are grounded on real-world searched knowledge.
MAKER: Solving Million-Step LLM Tasks
MAKER is the first system to successfully solve tasks requiring over one million LLM steps with zero errors, overcoming a fundamental limitation where LLMs typically fail after a few hundred steps in complex multi-step processes. The approach demonstrates that massively decomposed agentic processes can efficiently handle lengthy sequences of dependent logical operations through extreme decomposition and error correction.
LAMP: Language-Augmented Multi-Agent RL
LAMP integrates natural language processing into multi-agent reinforcement learning through a three-stage pipeline: Think (processes numerical data and identifies market patterns), Speak (generates strategic communications between agents), and Decide (synthesizes information into optimized policy). The framework achieves substantial improvements over baseline methods with +63.5% and +34.0% gains in cumulative return and +18.8% and +59.4% improvements in robustness, bridging traditional MARL with real-world economic contexts where language significantly influences decisions.
Olympiad-Level Formal Mathematical Reasoning with Reinforcement Learning
Google DeepMind introduces AlphaProof, an AlphaZero-inspired reinforcement learning agent that learns to find formal mathematical proofs within the Lean theorem prover, achieving the first-ever medal-level performance at the International Mathematical Olympiad by solving three problems, including the competition’s most difficult challenge.
The Era of Agentic Organization
Microsoft Research introduces asynchronous thinking (AsyncThink), a new reasoning paradigm where language models learn to organize their internal thinking into concurrently executable structures through an organizer-worker protocol, achieving 28% lower inference latency than parallel thinking while improving accuracy on mathematical reasoning and demonstrating zero-shot generalization to unseen tasks.