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AnySearch is a search infrastructure designed for AI agents. It supports real-time web search and vertical domain search, outputting structured Markdown that agents can directly use, improving search efficiency.
The article shares production learnings for reliably generating structured JSON output from LLMs, covering methods like JSON mode, schema validation, and retry loops, achieving 99.5% validity.
This paper investigates how message format (e.g., free text, JSON, triples) affects information loss across multiple hops in LLM agent relays, finding that format effects depend on the relay model's capability and that structure preserves content faithfully but does not correct errors.
This paper investigates how sign-branched repetition penalties cause structured-output corruption and gauge dependence across different models and inference frameworks, providing measurements and comparisons with alternative repetition controls.
Discusses methods for generating structured outputs from large language models using constrained sampling techniques.
IFStruct, an instruction-following benchmark for structured output by Liquid AI, now has a leaderboard on Hugging Face, aimed at improving small models for local execution and correct tool use.
ifstruct is an instruction-following benchmark for structured output by Liquid AI, designed to push the field toward better small models that can run locally.
A practical technique for improving structured output generation from LLMs by feeding validation errors back into retry prompts, allowing the model to self-correct rather than blindly retrying. The method involves describing the error in model-friendly terms and providing the previous output for editing.
The author shares five patterns that consistently kill AI agents: too many jobs per agent, no human-in-the-loop for destructive actions, unstructured outputs, no spend caps, and lack of uncertainty escalation paths. Practical guardrails and a checklist for reliable agent deployment are provided.
This paper identifies and analyzes 'tool suppression' in open-weight LLMs when both tool calling and JSON schema constraints are simultaneously enabled, proposing the Constraint Priority Inversion hypothesis and a mitigation strategy called Transparent Two-Pass Execution.
Analyzes how DiffusionGemma's bidirectional attention and parallel block generation could potentially yield higher valid tool call rates due to its ability to revise tokens, even though its base quality is lower than Gemma 4.
Discusses various methods to optimize DiffusionGemma inference, reduce hallucination, and improve performance for tool use and agents, including entropy-bounded sampling, schema scaffolding, and retrieval during denoising.
This article explains how to use GRPO to fine-tune an LLM (Qwen3-8B) for reliable JSON structured output, improving schema accuracy from 62% to 82%, surpassing GPT-4.1's 58%.
Experiments with a live agent processing market data through a governed runtime revealed three surprises: prompt structure drives execution reliability over reasoning quality; structured output can influence agent decisions; and separating reasoning and extraction into two calls maintains high parse success. The findings suggest governance belongs at the execution boundary, not on freeform reasoning.
A list of six powerful but lesser-known AI developer tools: Instructor for structured JSON output, Octopoda for agent memory, E2B for secure sandboxes, Firecrawl for website-to-markdown, Composio for app integrations, and LiteLLM for multi-model API.
This paper proposes Dynamic Infilling Anchors (DIA), a training-free method for diffusion large language models that dynamically estimates end-anchor positions to enforce format constraints (e.g., parseable JSON, reasoning templates) while avoiding the rigidity of fixed-span approaches. Experiments show significant zero-shot gains on GSM8K and MATH benchmarks.
The article questions why quantization benchmarks focus on perplexity and prose quality instead of tool call validity, arguing that structured outputs degrade earlier due to fewer valid token continuations, which could mislead practitioners about usable quant levels for agentic use.
User tested Gemma 4 2B running locally via LM Studio and Spring AI for structured JSON output, tool calling, and reasoning traces, finding it correctly identified a Java bug in code review and performed comparably to larger models.
The author details their experience building a custom agent loop using a small local model (Qwen3.5 9B) with structured workflows and a map-reduce pattern to manage context limits, replacing Claude Code for most tasks.
MaximeRivest explains DSPy's five core components—Optimizers, Signatures, LMs, Modules, and Adapters—and argues that effective AI engineering requires mastering these elements, highlighting the often-overlooked role of rendering structured outputs.