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Tool use, planning, multi-agent systems

01 ·Agents·★ MEMBER·PAPER·8 min read LLM Agents from Scratch — Designing the Tool-Use Loop Think, reach for a tool, look at what came back, think again. Working only from the text of the ReAct paper (Yao et al., 2022): why adding language to the action space is the whole idea, what function calling really implements, and the failure modes the authors actually measured — runaway loops and hallucinated tool calls. 02 ·Agents·★ MEMBER·PAPER·9 min read End-to-End Driving from Scratch — Perception to Control in a Single Network End-to-end autonomous driving learns everything from camera pixels to steering in one neural network. Starting from zero, we cover how it differs from the modular stack, imitation learning and distribution shift, why 'predicting the average' causes crashes, and why interpretability and validation remain the hard part. 03 ·Agents·FREE·11 min read MCP and Tool Protocols — The Standard That Connects an Agent's Hands When an LLM touches your calendar or your database, what is actually wired to what? From what a tool call really is, to the N×M problem MCP solves, to designing tool definitions, to the security boundary you cannot design around — starting from zero. 04 ·Agents·★ MEMBER·PAPER·10 min read Multi-Agent Design Patterns — Division, Debate, Verification Stack as many agents as you like — if they all fail the same way, you have one agent and a larger bill. The condition under which voting actually helps, written down, then the three patterns that follow from it: division of labour, debate, and adversarial verification — plus when one agent is enough. 05 ·Agents·★ MEMBER·PAPER·11 min read Designing Agent Memory — Short-Term, Long-Term, Episodic An LLM remembers nothing. Conversations only look continuous because you re-send the whole history every turn. Building up from zero: the three-layer split, the function that decides what gets recalled, and the part almost nobody designs — forgetting. 06 ·Agents·★ MEMBER·PAPER·11 min read Evaluating Agents — How Benchmarks and Harnesses Are Built An agent score is never a property of the model alone — it is a property of model plus harness plus environment plus grading rule. This piece opens up a single SWE-bench instance, shows why a tiny per-step gap becomes an order-of-magnitude gap over a long horizon, walks the four routes by which the answer leaks into the working environment, and sets out the conditions under which partial credit is safe. 07 ·Agents·★ MEMBER·PAPER·9 min read AlphaGo from Scratch — The Marriage of Search and Learning Starting from why Go was considered unsolvable for so long, this piece unpacks how the policy network, the value network, Monte Carlo tree search and self-play each cover the others' weaknesses — with the formulas and the code. It closes with what this design handed down to inference-time compute in LLMs. 08 ·Agents·★ MEMBER·PAPER·8 min read Build Your Own Agent Loop — The Minimal Shape of Tool Calling At the center of every AI agent is a single while loop. We build it from scratch without a framework — the shape of JSON function calls, what ReAct actually left behind, and the stopping conditions where nearly every incident originates. 09 ·Agents·★ MEMBER·PAPER·7 min read Paper Walkthrough: SWE-Bench ProMax — Measuring What Coding Agents Can Really Do with Large-Scale, Multilingual Refactoring A close read of the benchmark built to answer SWE-bench's saturation and grading defects: 170 expert-curated refactoring tasks across seven languages, averaging 11.4 modified files each. Why the best frontier model resolves only 41.2%, and why the dominant failure mode is refactoring that stops halfway. 10 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: Macaron-V1 — A Frozen Base plus a Mixture of LoRAs, Built to Keep Learning After Launch A guided tour of Mind Lab's open agent-model family Macaron-V1, straight from the paper: the Mixture-of-LoRA architecture that stacks four specialist adapters on a frozen 744B base and picks one per turn, and the recursive self-improvement loop that trains the model and its harness as a versioned pair. 11 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: EnvACE — Agents That Rehearse the World Instead of Calling It EnvACE trains a tool-using LLM agent without ever calling an external environment: the same policy alternates between acting and playing the environment. We walk through role-wise GRPO, test-time private rehearsal, results on four agent benchmarks, and the limitations. 12 ·Agents·★ MEMBER·PAPER·9 min read Paper Explained: Video-DeepResearch — Agents That Watch a Video, Then Chase Down Every Lead A walkthrough of Video-DeepResearch, which pulls visual clues out of a video and corroborates them with web search. We follow the primary source to see how two failure modes — a modality bias that makes models dodge visual tools, and knowledge leakage that lets them answer from memory alone — are beaten with stage-wise tool unlocking and a two-phase SFT + GRPO recipe. 13 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: ToolArtist — Search, Draw, or Redraw? The Image Agent That Decides for Itself Drawing "a harvester collecting dragon's blood resin on Socotra in 1955" correctly takes less artistic skill than research. ToolArtist makes searching, reasoning, and drawing all actions of a single policy — a fully agentic image generator. We walk through the conversion trick behind its SFT data, the dual-reward RL scheme (RAD-GRPO), and the 0.79 WISE result, straight from the paper. 14 ·Agents·★ MEMBER·PAPER·13 min read Paper Deep-Dive: Recursive Synthesis — Extending Verified Tasks Into 40,000 Long-Horizon Terminal Problems Long-horizon terminal tasks cost hundreds to thousands of dollars each to author. RST mass-produces them for about $0.05 by recursively extending already-verified seeds. Fifteen rounds yielded 37,484 tasks, with oracle solutions growing from a median of 67 lines to 374, and the resulting trajectories genuinely improved Qwen3.5 under SFT and PPO — a walkthrough of the data-synthesis paper from Tencent and collaborators. 15 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough: Qwen-UI-Agent — How Alibaba Built a GUI Agent That Works on Real Phones and PCs A guided tour of Alibaba MAI-UI team's technical report on Qwen-UI-Agent: a foundation GUI agent trained on 100+ physical phones, a hybrid GUI+CLI action space, and online RL over 100+ turn trajectories, reaching 92.2% on a real-device benchmark. 16 ·Agents·★ MEMBER·PAPER·10 min read Paper Explained: OSReward — Can You Trust the AI That Grades AI? Remeasuring Rewards for Computer-Use Agents The VLM judges that decide whether an agent driving a PC or a phone succeeded turn out to share a leniency bias that reads failure as success — a paper that exposes it with 1,019 human-verified trajectories and closes the gap with OS-Shepherd, an open reward model 30–60× cheaper, explained from zero background. 17 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: Metis — A 'Memory Foundation Model' That Moves Agent Memory Inside the Model Agent memory today is mostly bolted on from the outside via RAG. This paper proposes memory foundation models — models whose forward pass natively stores, forgets, and updates information — and builds Metis, the first prototype. A from-scratch walkthrough of how it works, how well it works, and where it breaks. 18 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough: MerchantBench — Can an LLM Agent Run an Online Store for a Year? Why It Earns Only 27.3% of What Humans Do A close read of MerchantBench, which measures the long-term coherence of LLM agents across a 365-day simulation of running an online store. Why the best configuration reaches only 27.3% of the average human's final net worth, read through three failure patterns: decaying activity, premature withdrawal, and policies frozen against the evidence. 19 ·Agents·★ MEMBER·PAPER·9 min read Paper Walkthrough: Mental World Modeling — A World Model That Advances Minds, Not Just Physics You can track a scene's physics perfectly and still get the next human action wrong. A walkthrough of Mental World Modeling (MWM), which carries beliefs, intentions, emotions and norms as state variables so that every action updates physics and mind together — plus Mentis, the training-free baseline, and its numbers across eight models, straight from the paper. 20 ·Agents·★ MEMBER·PAPER·8 min read Paper Explained: LongHorizon-Harness — Long-Horizon Agent Tasks Are a State-Management Problem, Not an Execution Problem Without touching the model at all, keeping task state outside execution and running a Manage-Execute-Audit loop dramatically improves long-horizon task performance — a walkthrough of Alibaba DreamX team's harness-design paper, from analogy to pseudocode. 21 ·Agents·★ MEMBER·PAPER·11 min read Paper Deep-Dive: Frontis-MA1 — Training the AI That Builds AI: One Step Toward Recursive Self-Improvement in ML Engineering Instead of training a model to solve tasks, train the operations that improve solutions — then run evolutionary search with those trained operations. A walkthrough of the OpenMLE stack and the 35B Frontis-MA1 model, straight from the paper: what's behind the 39.39%→71.21% jump on MLE-Bench Lite, how the search got cheaper and better at once, and the limits the authors themselves spell out. 22 ·Agents·★ MEMBER·PAPER·11 min read Paper Walkthrough: ComBodied Agents — Moving an Agent's Target from Software and Matter to the Person Digital agents rewrite software state; embodied agents rewrite physical state. So who models the person? Combodied Agents bind perception, longitudinal memory, a Personal World Model, and an intervention policy into one closed loop, and move the success criterion from task completion to whether the user's agency survived. A position paper with no experiments — here is its skeleton, and its limits. 23 ·Agents·★ MEMBER·PAPER·9 min read Paper Explained: Co-Evolution in Agentic Systems — Three Stages Toward Self-Directed Evolution Why do agents that are supposed to keep improving after deployment hit a ceiling? A ground-up walkthrough of a survey that organises the field into three stages — evolving peers, evolving environments, and an evolving evolution mechanism — with the defining equations, representative methods, and the open problems in evaluation and safety. 24 ·Agents·★ MEMBER·PAPER·13 min read Paper walkthrough: Zetta ζ — a robot harness that repairs itself mid-execution, with the policy frozen Existing embodied agents only reflect once an episode is over. Zetta instead runs code-based critics at action frequency and intervenes the moment a failure signature appears — without touching a single policy weight. A ground-up walkthrough of the design, following the paper itself. 25 ·Agents·★ MEMBER·PAPER·11 min read Paper walkthrough: StateM — 95.3% on Terminal-Bench 2.1 and a USD 15 run, without touching a single weight Long-horizon agents fail even when the model underneath can solve every individual step. StateM leaves the weights alone and hardens only the execution system around the agent, reporting 95.3% on Terminal-Bench 2.1 and a final scoring run billed at roughly USD 15 instead of USD 574.68. A ground-up walkthrough of the harness-scaling bet. 26 ·Agents·★ MEMBER·PAPER·13 min read Paper Explainer: SemaPLC — The Agent That Isn't Allowed to Say "Done" Most work on LLM-generated factory control code stops at showing that the code can run. SemaPLC forbids the agent from declaring completion until logged external checks confirm it, lifting live-runtime behavior from a baseline ceiling of 31.4 to 52.2. 27 ·Agents·★ MEMBER·PAPER·11 min read Paper Walkthrough: OmniScientist — An AI Scientist That Actually Looks at the Raw Data Automating every step of the research workflow doesn't help much if the only thing the agent ever sees is somebody else's summary table. OmniScientist puts raw waveforms, images and 3-D point clouds in front of the agent for the whole research lifecycle and enforces the gates in Python — walked through from the paper itself, up to the 21.7% finding buried in a seismic benchmark. 28 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough — FACET: Grounding Instruction, Environment, Solution and Verifier in One Executable State A terminal task is a four-part bundle: instruction, environment, reference solution, verifier. FACET builds and runs the environment first, then uses that realized state as shared ground for every other artifact. From 71K agent skills it produces 6,078 validated tasks, and 1.2K successful trajectories lift Qwen3.5 at 4B, 9B and 27B alike. 29 ·Agents·★ MEMBER·PAPER·9 min read Paper Explained: EnvHarness — Reshaping an Agent's Training World Without Rebuilding It The environments LLM agents train in are hand-built and frozen: blind to the agent's weaknesses and left behind as it improves. EnvHarness wraps a static environment in plug-in components that reshape its behavior without touching the underlying logic — and keep the original verifier intact. 30 ·Agents·★ MEMBER·PAPER·9 min read Paper Explainer: Why Agent Skills Work — and Where They Break Handing an agent a written procedure improves its scores. This paper dissects why, using 8,135 trial records and 528 matched trajectory triples. Skills work as procedural anchors rather than knowledge injection — and they break once the skill library grows. 31 ·Agents·★ MEMBER·PAPER·12 min read Paper Explained: Co-RL — Reasoning Without Labels, Emerging From a Diverse Cohort Grade your own answers long enough and the model collapses. Co-RL breaks that loop by rewarding each agent against a peer's majority vote, matching supervised training without touching a single ground-truth label. The mechanism, the dynamics, the numbers, and the traps — straight from the paper. 32 ·Agents·★ MEMBER·PAPER·9 min read Paper Walkthrough: SWE-bench Science — Can Coding Agents Fix Scientific Code? A walkthrough of SWE-bench Science, a repository-level benchmark of 119 tasks from 98 repos across 20 scientific domains. The best agent scores under 50% pass@1, and the paper names four recurring failure mechanisms plus an ablation showing scientific knowledge is not uniformly helpful. 33 ·Agents·★ MEMBER·PAPER·13 min read Paper Explained: FreeToken — Treating Your Own PC as a Single Elastic Inference Platform FreeToken is an edge-native serving system for frontier-scale MoE models on personal hardware. Its centerpiece is a q* policy that decides how many missed experts to ship over PCIe versus execute in place on the CPU — using nothing but two measured bandwidths. 34 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: Embodied-Navigator (TAMP-Nav) — Let the VLM Just Point, and Navigation Gets Both Faster and Better Instead of asking a VLM for 3D coordinates, have it point at a 2D pixel; think and store memory only at key nodes; align it all with two-level GRPO. A ground-up walkthrough of the design that hits 66.2% SR on R2R-CE at 16.58s per task using only 90k training trajectories. 35 ·Agents·★ MEMBER·PAPER·10 min read Paper walkthrough: ASI-Bench — peeling away human guidance to measure what AI can do alone ASI-Bench keeps the research goal, data and grading fixed while stripping away human methodological guidance one layer at a time. Average scores fall 50.91 → 29.10 → 26.62, and the place where things break is not method selection. 36 ·Agents·★ MEMBER·PAPER·8 min read Paper Walkthrough: FrontierChallenge — Grading Scientific Work on Whether It Was Actually Delivered A walkthrough of FrontierChallenge, a benchmark that scores whether an agent finishes a scientific workflow. Average scores hit 87.9 while full completion stayed at 20.6% — and 0% in electrochemistry, where the average was 94.9. Worse, 75.5% of failing trajectories still ended by claiming they were done. 37 ·Agents·★ MEMBER·PAPER·13 min read Paper Explained: JIT-Agent — A Model That Writes the Agent Harness On Demand An agent's capability is not the model's alone. This is a ground-up walkthrough of JIT-Agent, a model trained to synthesize the four-module harness — memory, planning, action, tools — freshly for each task. 38 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: ZimaBlue — Turning 120,000 Hours of Egocentric Video into Robot Skill A ground-up walkthrough of the World Action Model that converts 120,000 hours of action-free egocentric video into robot control: a three-stage curriculum, a 100-D unified action interface, and an asynchronous Slow-Fast pair that takes zero-shot success from 36.1% to 77.8% at a 33 ms control loop. 39 ·Agents·★ MEMBER·PAPER·11 min read Paper Explained: What Makes Good Agentic Data? The ACE Lens A survey that recasts agentic training data as a four-part object (environment, task, interaction, verifier) and reframes generation as constrained distribution design: admit on Accuracy, place mass by Complexity, spread coverage with divErsity. 40 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough — UrbanGround: Where MLLM Agents Break Down on a Real Street Drop an MLLM agent into a real-scale replica of Hong Kong built from territory-wide 3D geospatial data. Visual recognition clears 90%, orientation sits near 40%, long-range navigation is close to 0%. A walkthrough of the benchmark that measures the gap between seeing and moving. 41 ·Agents·★ MEMBER·PAPER·13 min read Paper Walkthrough: UI-Venus-2 — Taking Screen-Operating Agents From Benchmarks to Real Work A ground-up read of the UI-Venus-2 Technical Report: a GUI agent that drives phones, browsers and desktops from pixels alone. What it scales, how it verifies its own training data, and where it still falls short. 42 ·Agents·★ MEMBER·PAPER·15 min read Paper Walkthrough: Training Agents to Evolve with Their Harness Why compact models break when you rewrite prompts and tool definitions every week, and what the team behind Taobao Live's AI avatar streamers does about it. A ground-up walkthrough of Harness-Aware Training, from intuition to equations to measured numbers and stated limits. 43 ·Agents·★ MEMBER·PAPER·13 min read Paper Explained: StarHarness — Evolving the Scaffold Instead of the Weights Freeze the model weights and search over the harness instead — prompts, tool schemas, skills, MCP providers, subagents, agent loop. Across three enterprise benchmarks this bought 20–35 points, held on tasks never used for evolution, and transferred to other models unchanged. 44 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough: SecOPD — Grading One Token at a Time to Cut Adaptive Prompt Injection by an Order of Magnitude Defensively fine-tuned LLMs still fall to adaptive prompt injections at close to 100%. The culprit is training that hands the whole response a single score. SecOPD grades every token using a teacher that never saw the injection — explained from first principles. 45 ·Agents·★ MEMBER·PAPER·12 min read Paper Explained: Repo-To-Skill — Distilling GitHub Repositories Into Skills an AI Can Use A third layer that is neither the model nor the harness: operational knowledge, distilled from 1,000 GitHub repositories into 5,353 verified skills. What DisCo and the AREX-Skill Library actually do, what 31.11%→72.89% on MLE-bench is made of, and where the skills failed. 46 ·Agents·★ MEMBER·PAPER·15 min read Paper Walkthrough: PILOT in the Loop — Fixing the Run While It Is Still Running Agent self-improvement that only starts after execution ends arrives too late. A ground-up walkthrough of PILOT, a supervisor–worker harness that redirects the live run while distilling skills from it, from intuition to equations to measured numbers and stated limits. 47 ·Agents·★ MEMBER·PAPER·11 min read Paper Walkthrough: LoopArena — Benchmarking the Model That Steers a Coding Agent A ground-up walkthrough of LoopArena, a benchmark that scores the outer loop steering a coding agent while holding the agent itself fixed — including how to read the headline result of 24.69% strict success on full tasks. 48 ·Agents·★ MEMBER·PAPER·14 min read Paper Walkthrough: Code World Model — Putting a Coding Agent in Charge of the World A ground-up reading of Code World Model, which hands the *evolution* of a world to executable code and its *appearance* to a video model — including the proxy interface that joins them, and the limits the authors admit to. 49 ·Agents·★ MEMBER·PAPER·13 min read Paper Walkthrough: Code as Worlds — An Agent That Writes the World Down as Runnable Code A close read of Code-as-World, which represents physical worlds as executable code and searches for world hypotheses through a propose–execute–render–verify loop. The three parts of an EWR, the agentic discovery loop, the QuantiPhy results, and the limits the paper itself admits. 50 ·Agents·★ MEMBER·PAPER·11 min read Paper Walkthrough: AutoSaddler — Growing a Harness That Doesn't Break, from Agent Failure Logs A ground-up walkthrough of AutoSaddler, which automatically optimizes the harness around an LLM agent — prompts, tools, and middleware — by repeatedly diagnosing failure traces and generating structured patches. It beat the base harnesses on GAIA2, SWE-Bench Pro, and Terminal-Bench 2.0 by 9.0, 9.6, and 10.0 points. 51 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: HarnessDev — Can an LLM Build and Maintain the System It Runs Inside? A ground-up walkthrough of HarnessDev, a benchmark that asks whether an LLM can build an agent harness from a deliberately useless seed and then improve it from execution feedback. Models can build one; the gains rarely survive contact with held-out tasks or a different runtime model. 52 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: EarlyEval — Making Agent Evaluation Cheaper by Stopping Early One evaluation pass over an agentic benchmark costs hundreds of dollars. EarlyEval exploits the fact that an agent's ending is legible from its middle, halting runs to cut 13–26% of steps. Mechanism, numbers, and limits, straight from the paper. 53 ·Agents·★ MEMBER·PAPER·14 min read Paper Walkthrough: Aspire — Can Models Self-Evolve from Vague Goals? Hand an agent nothing but "get better at mathematical reasoning" and let it decide what to learn and how to check itself. Aspire measures the result on 520 hidden, expert-written items — and finds that agents close the training loop far more reliably than the capability loop. 54 ·Agents·★ MEMBER·PAPER·12 min read Paper Walkthrough: Terminal-Universe — Turning Agent Logs Back Into Reusable Execution Environments Replay the file operations recorded in an agent trajectory, have a completion agent fill in what's missing, and you get an executable workspace back. The pipeline yields 37.3k environments and lifts Qwen3.5-27B by 11.9 points on Terminal-Bench 2.1. 55 ·Agents·★ MEMBER·PAPER·9 min read CogEvol: What the Reward Cannot Measure, RL Will Quietly Destroy A technical report on a model family that generates teaching material in a single pass. Its centerpiece is an incident the authors disclose in full: a screenshot-only reward taught the policy to ship games that looked convincing and could not be played.