AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Composer 2
Cursor releases the technical report for Composer 2, a specialized model designed for agentic software engineering that demonstrates strong long-term planning and coding intelligence while maintaining efficiency for interactive use. The report details a process for training domain-specialized models that starts with continued pretraining and scales up with reinforcement learning.
BIGMAS
Even the best reasoning models hit an accuracy collapse beyond a certain problem complexity. BIGMAS (Brain-Inspired Graph Multi-Agent Systems) organizes specialized LLM agents as nodes in a dynamically constructed directed graph, coordinating exclusively through a centralized shared workspace inspired by global workspace theory from cognitive neuroscience. A GraphDesigner agent analyzes each problem instance and produces a task-specific directed agent graph together with a workspace contract. The framework constructs structurally distinct graphs whose complexity tracks task demands, from compact three-node pipelines for simple arithmetic to nine-node cyclic structures for multi-step planning. BIGMAS consistently improves reasoning performance for both standard LLMs and large reasoning models, outperforming existing multi-agent baselines.
OpenDev
Terminal-native coding agents represent a fundamental shift in how developers interact with AI assistance. OpenDev is an open-source, command-line coding agent that operates where developers already manage source control and deploy environments, offering a comprehensive 81-page technical report on scaffolding, harness design, context engineering, and lessons learned from building production coding agents.
KARL
Databricks presents KARL, a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. The work also introduces KARLBench, a new evaluation framework spanning six search domains.
AgentIR
Deep research agents generate explicit reasoning before every search call, but existing retrievers completely ignore these rich signals about search intent and problem context. AgentIR introduces reasoning-aware retrieval that jointly embeds the agent’s reasoning trace alongside its query, along with DR-Synth, a data synthesis method for generating training data from standard QA datasets. On BrowseComp-Plus, AgentIR-4B achieves 68% accuracy with Tongyi-DeepResearch compared to 50% with conventional embedding models twice its size and 37% with BM25.
Think Harder or Know More
This paper investigates transformer models featuring both adaptive per-layer looping, where each block learns to iterate its hidden state via a learned halting mechanism, and gated memory banks that provide additional learned storage. The key finding is that looping primarily benefits mathematical reasoning while memory banks help recover performance on commonsense tasks. Combining both mechanisms yields a model that outperforms an iso-FLOP baseline with three times the number of layers on math benchmarks. Analysis of model internals reveals layer specialization: early layers loop minimally and access memory sparingly, while later layers do both more heavily.
Bayesian Teaching for LLMs
Google researchers introduce a method to teach LLMs to reason like Bayesians by fine-tuning on interactions with a Bayesian Assistant that represents optimal probabilistic inference. LLMs normally fall far short of normative Bayesian reasoning, but this training approach dramatically improves their ability to update predictions based on new evidence.
Theory of Mind in Multi-Agent LLMs
This work introduces a multi-agent architecture combining Theory of Mind (ToM), Belief-Desire-Intention (BDI) models, and symbolic solvers for logical verification, evaluating it on resource allocation problems across multiple LLMs. The central finding is counterintuitive: simply adding cognitive mechanisms does not automatically improve coordination.
Numina-Lean-Agent
Numina-Lean-Agent proposes a paradigm shift in automated theorem proving: instead of building complex, multi-component systems with heavy computational overhead, it directly uses a general coding agent as a formal math reasoner. Combining Claude Code with Numina-Lean-MCP, the system autonomously interacts with the Lean proof assistant while accessing theorem libraries and auxiliary reasoning tools.
ParamMem
Self-reflection enables language agents to iteratively refine solutions, but models tend to generate repetitive reflections that add noise instead of useful signal. ParamMem introduces a parametric memory module that encodes cross-sample reflection patterns into model parameters, enabling diverse reflection generation through temperature-controlled sampling.
Auton Agentic AI Framework
Snap Research introduces the Auton framework, a declarative architecture for specification, governance, and runtime execution of autonomous agent systems. It addresses a fundamental mismatch: LLMs produce stochastic, unstructured outputs, while backend infrastructure requires deterministic, schema-conformant inputs.
Reaching Agreement Among LLM Agents
This paper introduces Aegean, a consensus protocol that frames multi-agent refinement as a distributed consensus problem. Rather than static heuristic workflows with fixed loop limits, Aegean enables early termination when sufficient agents converge, achieving 1.2-20x latency reduction across four mathematical reasoning benchmarks while maintaining answer quality within 2.5%. The consensus-aware serving engine performs incremental quorum detection across concurrent agent executions, cutting wasted compute on stragglers.
Phi-4-reasoning-vision-15B
Microsoft presents Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model that combines visual understanding with structured reasoning capabilities. Trained on just 200 billion tokens of multimodal data, the model excels at math and science reasoning and UI comprehension while requiring significantly less compute than comparable open-weight VLMs. The key insight is that systematic filtering, error correction, and synthetic augmentation remain the primary levers for model performance, pushing the Pareto frontier of the accuracy-compute tradeoff.
Deep-Thinking Tokens
Google researchers challenge the assumption that longer outputs indicate better reasoning. They introduce deep-thinking tokens, a metric that identifies tokens where internal model predictions shift significantly across layers before stabilizing. Unlike raw token count, which negatively correlates with accuracy (r = -0.59), the deep-thinking ratio shows a robust positive correlation (r = 0.683).
Discovering Multi-Agent Learning Algorithms with LLMs
Google DeepMind uses AlphaEvolve, an evolutionary coding agent powered by LLMs, to automatically discover new multi-agent learning algorithms for imperfect-information games. Rather than relying on manual algorithm design, the system navigates vast algorithmic design spaces and discovers non-intuitive mechanisms that outperform state-of-the-art baselines.
Doc-to-LoRA
Sakana AI introduces Doc-to-LoRA (D2L), a lightweight hypernetwork that meta-learns to compress long documents into LoRA adapters in a single forward pass. Instead of processing long contexts through expensive quadratic attention, D2L converts the document into parameter-space representations that the target LLM can use without re-consuming the original text.
CoT Faithfulness via REMUL
Researchers propose REMUL, a training approach for making chain-of-thought reasoning more faithful and monitorable. A speaker model generates reasoning traces that multiple listener models attempt to follow and complete, using RL to reward reasoning that is understandable to other models. Tested across BIG-Bench Extra Hard, MuSR, ZebraLogicBench, and FOLIO, REMUL improves three faithfulness metrics while also boosting overall accuracy, producing shorter and more direct reasoning chains.
MemoryArena
MemoryArena introduces a benchmark for evaluating how agents utilize memory across multiple interconnected sessions. The key finding is that scoring well on memory recall does not mean an agent can actually use that memory to take correct actions across sessions. Models with near-saturated performance on existing benchmarks like LoCoMo perform poorly in agentic multi-session settings.
CogRouter
CogRouter enables adaptive reasoning depth for LLM agents by dynamically selecting from four hierarchical cognitive levels at each step, from instinctive responses to strategic planning. Using confidence-aware advantage reweighting during training, Qwen2.5-7B with CogRouter achieves 82.3% success rate on agentic benchmarks, substantially outperforming larger models while consuming fewer tokens by skipping heavy reasoning on routine steps.
Team of Thoughts
Team of Thoughts presents a multi-agent framework for efficient test-time scaling through orchestrated tool calling. The system uses an orchestrator tool design where agents with different capabilities are coordinated by a calibrated orchestrator. With self-assessment for tool agents and orchestrator calibration for identifying superior coordination models, Team of Thoughts achieves 96.67% on AIME24 and 72.53% on LiveCodeBench, substantially exceeding homogeneous baselines.
LLaDA 2.1
Ant Group releases LLaDA 2.1, a major upgrade to discrete diffusion language models that breaks the speed-quality trade-off through Token-to-Token (T2T) editing. By weaving token editing into the conventional Mask-to-Token decoding scheme, LLaDA 2.1 introduces two configurable modes: Speedy Mode for aggressive throughput and Quality Mode for benchmark-leading accuracy. The release also includes the first large-scale RL framework for diffusion LLMs.
SkillRL
SkillRL introduces a recursive skill-augmented RL framework that bridges the gap between raw experience and policy improvement through automatic skill discovery. Instead of storing noisy raw trajectories, SkillRL distills experience into reusable high-level behavioral patterns and evolves them alongside the agent policy during training.
InftyThink+
InftyThink+ is an end-to-end RL framework for infinite-horizon reasoning that optimizes the entire iterative reasoning trajectory. Standard long chain-of-thought suffers from quadratic cost, context length limits, and lost-in-the-middle degradation. InftyThink+ addresses all three by letting models autonomously decide when to summarize, what to preserve, and how to resume, trained through trajectory-level reinforcement learning.
Agyn
Agyn is a fully automated multi-agent system that models software engineering as an organizational process rather than a monolithic code generation task. Built on an open-source platform for configuring agent teams, the system assigns specialized agents to distinct roles and follows a structured development methodology - all without human intervention. Notably, Agyn was designed for real production use and was not tuned for the SWE-bench.