AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Hierarchical Reasoning Model
Hierarchical Reasoning Model (HRM) is a novel, brain-inspired architecture that replaces CoT prompting with a recurrent model designed for deep, latent computation. It departs from token-level reasoning by using two coupled modules: a slow, high-level planner and a fast, low-level executor, achieving greater reasoning depth and efficiency with only 27M parameters and no pretraining. Despite its small size and minimal training data (~1k examples), HRM solves complex tasks like ARC, Sudoku-Extreme, and 30×30 maze navigation, where CoT-based LLMs fail.
Self-Evolving Agents
This survey offers a comprehensive review of self-evolving agents, framing the field around what, when, and how agents evolve across models, memory, tools, and interactions. It highlights adaptation mechanisms, evaluation methods, and real-world applications, positioning self-evolution as a key step toward achieving Artificial Super Intelligence (ASI).
Efficient Attention Mechanisms
This survey reviews linear and sparse attention techniques that reduce the quadratic cost of Transformer self-attention, enabling more efficient long-context modeling. It also examines their integration into large-scale LLMs and discusses practical deployment and hardware considerations.
Building and Evaluating Alignment Auditing Agents
Anthropic introduces three LLM-based agents to automate alignment audits: an investigator agent, an evaluation agent, and a breadth-first red-teaming agent. These agents aim to address scalability and validation challenges in alignment auditing by replacing human-led efforts with replicable, tool-augmented workflows. Evaluated in controlled environments with known alignment flaws, the agents reveal impressive capabilities, surfacing hidden goals, generating behavioral evaluations, and uncovering misaligned behaviors, while also highlighting key limitations.
Structural Planning for LLM Agents
This paper introduces Routine, a structured planning format designed to improve the stability and accuracy of LLM agents executing multi-step tool-calling tasks in enterprise settings. Traditional agent planning approaches often fail in enterprise scenarios due to unstructured plans, weak instruction following, and tool selection errors. Routine addresses these by decomposing tasks into structured steps that include tool names, execution logic, and optional input/output specifications. The authors evaluate Routine on real-world HR scenarios and show strong performance gains in both open and fine-tuned models.
Towards Compute-Optimal Many-Shot In-Context Learning
Proposes practical strategies for reducing the cost of many-shot in-context learning while preserving or improving performance. With long-context LLMs like Gemini Pro and Flash supporting thousands of demonstrations, caching becomes essential, yet naïvely using only random demonstrations misses potential accuracy gains from smarter selection.
Context Rot
This comprehensive study by Chroma evaluates how state-of-the-art LLMs perform as input context length increases, challenging the common assumption that longer contexts are uniformly handled. Testing 18 top models (including GPT-4.1, Claude 4, Gemini 2.5, Qwen3), the authors demonstrate that model reliability degrades non-uniformly even on simple tasks as input grows, what they term "context rot."
Chain-of-Thought Monitorability
Proposes that language-based CoT reasoning in LLMs offers an opportunity for AI safety by enabling automated oversight of models’ internal reasoning processes. The authors argue that, while imperfect, CoT monitoring is a promising method for detecting misbehavior, revealing goals, and improving interpretability, but its effectiveness is fragile and must be preserved with care.
A Survey of Context Engineering for LLMs
This survey defines Context Engineering as a formal discipline for optimizing information given to LLMs, outlining its core components, retrieval, processing, and management, and their integration in systems like RAG, memory, and multi-agent frameworks. It identifies a key gap: LLMs can understand complex input but struggle to generate equally complex long-form output, pointing to a major direction for future research.
MemAgent
Introduces an RL–driven memory agent that enables transformer-based LLMs to handle documents up to 3.5 million tokens with near lossless performance, linear complexity, and no need for architectural modifications.
AI Research Agents for Machine Learning
Presents a new framework, AIRA-dojo, for developing and evaluating AI research agents. They use this framework to systematically investigate the components of successful AI agents on the MLE-bench benchmark, a challenging set of real-world machine learning problems from Kaggle. Key findings:
Threats in LLM-Powered AI Agents Workflows
This work presents the first comprehensive, end-to-end threat model for LLM-powered agent ecosystems. As LLM agents gain the ability to orchestrate multi-step workflows and interact via protocols like MCP, ANP, and A2A, this paper surveys over 30 attack techniques spanning the entire stack, from input manipulation to inter-agent protocol exploits.
Survey on Evaluation of LLM-based Agents
This work presents the first comprehensive overview of how to evaluate LLM-based agents, which differ significantly from traditional LLMs by maintaining memory, planning over multiple steps, using tools, and interacting with dynamic environments. The authors categorize and analyze the evaluation landscape across four axes: core agent capabilities, application-specific agent benchmarks, generalist agent evaluation, and supporting evaluation frameworks.
MEM1
This work introduces MEM1, an RL framework for training language agents that operate efficiently over long-horizon, multi-turn tasks by learning to consolidate memory and reasoning into a compact internal state. Unlike traditional agents that append all past interactions, leading to ballooning memory usage and degraded performance, MEM1 maintains a constant memory size by discarding obsolete context after each reasoning step. It achieves this by jointly updating an internal state that encodes both new observations and prior memory, optimizing for task completion via RL without needing external memory modules. Key contributions and findings:
DeepRare
Introduces DeepRare, a modular agentic system powered by LLMs to aid rare disease diagnosis from multimodal clinical inputs (text, HPO terms, VCFs). It generates ranked diagnostic hypotheses with fully traceable reasoning chains linked to verifiable medical sources, addressing a long-standing need for interpretability in clinical AI.
AlphaGenome
Google DeepMind introduces AlphaGenome, a powerful AI model designed to predict how genetic variants affect gene regulation by modeling up to 1 million DNA base pairs at single-base resolution. Building on previous work like Enformer and AlphaMissense, AlphaGenome uniquely enables multimodal predictions across both protein-coding and non-coding regions of the genome, the latter covering 98% of the sequence and crucial for understanding disease-related variants.
AI Agent Communication Protocols
This paper presents the first comprehensive survey on security in LLM-driven agent communication, categorizing it into three stages: user-agent interaction, agent-agent communication, and agent-environment communication. It details protocols, security threats (e.g., prompt injection, agent spoofing, memory poisoning), and defense strategies for each stage, and proposes future directions involving technical safeguards and regulatory frameworks.
Eliciting Reasoning with Cognitive Tools
Proposes a modular, tool-based approach to eliciting reasoning in LLMs, inspired by cognitive science. Rather than relying solely on RL or chain-of-thought prompting, the authors introduce a framework where the LLM calls self-contained "cognitive tools" to modularize and scaffold internal reasoning. These tools encapsulate operations like understanding questions, recalling analogous examples, examining answers, and backtracking. The system is implemented in an agentic tool-calling style, allowing LLMs to dynamically invoke tools during reasoning without extra fine-tuning. Highlights:
SHADE-Arena
This paper introduces SHADE-Arena, the first large-scale benchmark for evaluating whether LLM-based agents can subtly carry out harmful side objectives (sabotage) while avoiding detection by monitors. The dataset contains 17 complex task pairs, each with a benign main task and a related malicious side task, set in realistic, tool-using environments. Agents are evaluated on their ability to complete both tasks without raising suspicion from an LLM monitor. Key Findings:
New Lens on RAG Systems
Introduces a new conceptual and empirical framework for analyzing RAG systems through the lens of sufficient context, whether the retrieved content alone enables answering a query. This notion helps decouple retrieval failures from generation errors in LLMs, providing clarity on model behavior under different contextual adequacy. Key findings:
Open-Ended Evolution of Self-Improving Agents
This work presents the Darwin Gödel Machine (DGM), a system that advances the vision of self-improving AI by combining self-referential code modification with open-ended evolutionary search. Unlike the original Gödel machine, which requires provable benefits for code changes (a practically intractable constraint), the DGM adopts an empirical approach: it modifies its own codebase and evaluates improvements on coding benchmarks. Key contributions and findings:
An Operating System for Memory-Augmented Generation in LLMs Introduces a unified operating system for managing memory LLMs, addressing a key limitation in
current architectures: their lack of structured, persistent, and governable memory. While today's LLMs rely primarily on parametric memory (model weights) and limited short-term context, MemOS proposes a comprehensive memory lifecycle and management infrastructure designed to support continual learning, behavioral consistency, and knowledge evolution. Key contributions and components include:
QwenLong-L1
A new reinforcement learning framework that scales large reasoning models (LRMs) from short to long contexts using progressive context scaling and hybrid rewards. It achieves top performance on seven long-context benchmarks, surpassing models like OpenAI-o3-mini and Qwen3-235B-A22B, and matching Claude-3.7-Sonnet-Thinking, demonstrating strong reasoning with up to 120K token inputs.
EfficientLLM
Introduces the first large-scale, empirical benchmark for evaluating efficiency trade-offs in LLMs across architecture, fine-tuning, and inference. Conducted on a high-performance cluster (48×GH200, 8×H200 GPUs), the study evaluates over 100 model–technique pairs spanning 0.5B–72B parameters, using six metrics: memory utilization, compute utilization, latency, throughput, energy consumption, and compression rate. Key insights include: