GBNF grammars make it structurally impossible for a local model to emit an invalid tool call. Here's the mechanism that turns small models into reliable agents.
fx - Tiny, open, native coding agent
Tiny, open, native coding agent. A minimal CLI written in Zig, optimized for research and embeddability as part of larger systems.
Qwen3-Coder: Agentic Coding in the World
GITHUB HUGGING FACE MODELSCOPE DISCORD Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters which supports the context length of 256K tokens natively and 1M tokens with extrapolation methods, offering exceptional performance in both coding and agentic tasks. Qwen3-Coder-480B-A35B-Instruct sets new state-of-the-art results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use, comparable to Claude Sonnet 4.
Structured CoT: Shorter Reasoning with a Grammar File
Constrain only the think block with a tiny grammar. On Qwen3.6 coding evals, explicit reasoning gets 22x-43x shorter without losing pass@1 in these runs.
Caveman | Lithic Token Compression
A semantic constraint engine for Claude Code & Codex. Forces agentic communication into minimal-token lithic structures. Retain 100% technical accuracy while destroying up to 87% of output latency.
Serpentine software
I don’t usually write hot takes. But StrongDM’s Software Factory post is worth it, so here goes. Their post is elegant, short, and well-written, so I’m going to just go through a bunch of pull quotes. Compounding correctness long-horizon agentic coding workflows [now] compound correctness rather than error This is a big, empirical claim. I’m already convinced by my own experiences that agents, suitably scaffolded, can form a Brownian ratchet, such that pouring on more compute helps, on balance.
The Agent Harness
A specification for agent behavior that LLM frameworks leave undefined: error handling, context management, tool execution, and state transitions.
MCP Agent Fine tuning Workshop - Ronan McGovern
Cantrip: On summoning entities from language in circles | deepfates
With Cantrip, deepfates reimagines the fundamentals of language model agents. Available as a ghost library with generative test specification.
Cantrip: On summoning entities from language in circles | deepfates
With Cantrip, deepfates reimagines the fundamentals of language model agents. Available as a ghost library with generative test specification.
Agentic systems are adept at solving well-scoped, verifiable problems in computational biology
We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation.
Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs
We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding. It asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively. Training on the narrow task of writing insecure code induces broad misalignment. We call this emergent misalignment. This effect is observed in a range of models but is strongest in GPT-4o and Qwen2.5-Coder-32B-Instruct. Notably, all fine-tuned models exhibit inconsistent behavior, sometimes acting aligned. Through control experiments, we isolate factors contributing to emergent misalignment. Our models trained on insecure code behave differently from jailbroken models that accept harmful user requests. Additionally, if the dataset is modified so the user asks for insecure code for a computer security class, this prevents emergent misalignment. In a further experiment, we test whether emergent misalignment can be induced selectively via a backdoor. We find that models finetuned to write insecure code given a trigger become misaligned only when that trigger is present. So the misalignment is hidden without knowledge of the trigger. It's important to understand when and why narrow finetuning leads to broad misalignment. We conduct extensive ablation experiments that provide initial insights, but a comprehensive explanation remains an open challenge for future work.
Readwise MCP Server - Readwise Docs
The Model Context Protocol (MCP) standardizes how applications provide context to Large Language Models (LLMs). The Readwise MCP was created to act as a...
Code execution with MCP: building more efficient AI agents
Learn how code execution with the Model Context Protocol enables agents to handle more tools while using fewer tokens, reducing context overhead by up to 98.7%.
Packrat parsing: | Proceedings of the seventh ACM SIGPLAN international conference on Functional programming
For decades we have been using Chomsky's generative system of grammars, particularly context-free grammars (CFGs) and regular expressions (REs), to express the syntax of programming languages and protocols. The power of generative grammars to express ...
On the Coming Industrialisation of Exploit Generation with LLMs
Recently I ran an experiment where I built agents on top of Opus 4.5 and GPT-5.2 and then challenged them to write exploits for a zeroday vulnerability in the QuickJS Javascript interpreter. I adde…
