AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
APIGen
presents an automated data generation pipeline to synthesize high-quality datasets for function-calling applications; shows that 7B models trained on curated datasets outperform GPT-4 models and other state-of-the-art models on the Berkeley Function-Calling Benchmark; a dataset consisting of 60K entries is also released to help with research in function-calling enabled agents.
Agentless
introduces OpenAutoEncoder-Agentless which offers an agentless system that solves 27.3% GitHub issues on SWE-bench Lite; claims to outperform all other open-source AI-powered software engineering agents.
AI Agents That Matter
analyzes current agent evaluation practices and reveals shortcomings that potentially hinder real-world application; proposes an implementation that jointly optimizes cost and accuracy and a framework to avoid overfitting agents.
GraphReader
proposes a graph-based agent system to enhance the long-context abilities of LLMs; it structures long text into a graph and employs an agent to explore the graph (using predefined functions guided by a step-by-step rational plan) to effectively generate answers for questions; consistently outperforms GPT-4-128k across context lengths from 16k to 256k.
Tree Search for Language Model Agents
proposes an inference-time tree search algorithm for LM agents to perform exploration and enable multi-step reasoning; it’s tested on interactive web environments and applied to GPT-4o to significantly improve performance; demonstrates that performance scales when increasing test-time compute.
SelfGoal
a framework to enhance an LLM-based agent's capabilities to achieve high-level goals; adaptively breaks down a high-level goal into a tree structure of practical subgoals during interaction with the environment; improves performance on various tasks, including competitive, cooperative, and deferred feedback environments
Mixture-of-Agents
an approach that leverages the collective strengths of multiple LLMs through a Mixture-of-Agents methodology; layers are designed with multiple LLM agents and each agent builds on the outputs of other agents in the previous layers; surpasses GPT-4o on AlpacaEval 2.0, MT-Bench and FLASK.
AgentGym
a new framework featuring various environments and tasks for broad, real-time, and concurrent agent exploration; builds a generally capable LLM-based agent with self-evolution abilities and explores its potential beyond previously seen data across tasks and environments.
Agent Planning with World Knowledge Model
introduces a parametric world knowledge model to facilitate agent planning; the agent model can self-synthesize knowledge from expert and sampled trajectories; this is used to train the world knowledge model; prior task knowledge is used to guide global planning and dynamic state knowledge is used to guide the local planning; demonstrates superior performance compared to various strong baselines when adopting open-source LLMs like Mistral-7B and Gemma-7B.
AlphaMath Almost Zero
enhances LLMs with Monte Carlo Tree Search (MCTS) to improve mathematical reasoning capabilities; the MCTS framework extends the LLM to achieve a more effective balance between exploration and exploitation; for this work, the idea is to generate high-quality math reasoning data without professional human annotations; the assumption is that a well pre-trained LLM already possesses mathematical knowledge to generate reasoning steps but needs better stimulation such as an advanced prompting or search strategy; unlike other methods such as Program-of-thought and Chain-of-thought, no solutions are required for the training data, just the math questions and the answers; the integration of LLMs, a value model, and the MCTS framework enables an effective and autonomous process of generating high-quality math reasoning data; the value model also aids the policy model in searching for effective solution paths.
AutoCrawler
AutoCrawler is a two-stage framework that combines LLMs with the hierarchical structure of HTML to auto-generate reusable web scrapers. Wrapper-based scrapers break on new sites and pure LLM agents don't reuse well across pages; AutoCrawler addresses both limitations.
Self-Evolution of LLMs
This survey organizes the emerging literature on self-evolving LLMs - models that improve through their own generated experience rather than additional human supervision. The authors propose a unified four-phase cycle and taxonomize existing methods across both standalone models and agent systems.
Mixtral 8x22B
Mistral's Mixtral 8x22B is a sparse Mixture-of-Experts model with 141B total / 39B active parameters and a 64K context window, released under Apache 2.0. It leads open models on MMLU and posts strong math, code, and multilingual numbers.
A Survey on Retrieval-Augmented Text Generation for LLMs
This survey organizes the RAG literature into a four-stage framework (pre-retrieval, retrieval, post-retrieval, generation) and traces the paradigm's evolution alongside open challenges.
The Illusion of State in State-Space Models
This paper proves that modern state-space models (Mamba, S4, etc.) share the same expressive ceiling as transformers: they cannot compute anything outside the TC^0 complexity class, despite the RNN-like "state" vocabulary they borrow.
Emerging AI Agent Architectures
A short survey mapping the current landscape of LLM-based agent architectures, focused on reasoning, planning, and tool calling as the three capability pillars for complex agentic workflows.
Leave No Context Behind (Infini-attention)
Google's Infini-attention extends Transformer LLMs to effectively infinite context with bounded memory and compute. It blends a compressive memory module with both masked local attention and linear long-term attention inside a single Transformer block.
OpenEQA
Meta's OpenEQA is an open-vocabulary benchmark for embodied question answering: 1,600+ human-written questions across 180+ real-world environments, with a calibrated LLM-as-judge metric that tracks human agreement closely.
SWE-Agent
Princeton's SWE-agent pairs a language model with a custom agent-computer interface (ACI) that exposes file navigation, editing, and test execution as discrete tools, letting the agent autonomously resolve real GitHub issues.
Grok-1.5
xAI's Grok-1.5 is the successor to the open-weight Grok-1, emphasizing long-context understanding and substantially stronger math, code, and reasoning performance.
Long-form factuality in LLMs
Google DeepMind introduces LongFact and SAFE, a prompt set and automated evaluator for judging whether the long-form answers of modern LLMs are actually factual.
Agent Lumos
Lumos is a unified recipe for training open-source LLM agents that separates high-level planning from low-level grounding so each module can be supervised and improved independently.
AIOS
AIOS treats the LLM as the "brain" of an operating-system kernel for agents, providing scheduling, memory, storage, tool, and access-control services so agent apps can share resources safely.
What Are Tools Anyway? A Survey of Tool Use in LLMs
This survey establishes a formal definition of tools as "external programs used by LMs" and systematizes when, why, and how tool-use improves LLM performance.