Researchers from Stanford, EPFL, and UNC introduce Weak-for-Strong Harnessing, W4S, a new Reinforcement Learning RL framework that trains a small meta-agent to design and refine code workflows that call a stronger executor model. The meta-agent does not fine tune the strong model, it learns to orchestrate it. W4S formalizes workflow design as a multi turn […]
The post Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs appeared first on MarkTechPost.
Microsoft Research proposes BitNet Distillation, a pipeline that converts existing full precision LLMs into 1.58 bit BitNet students for specific tasks, while keeping accuracy close to the FP16 teacher and improving CPU efficiency. The method combines SubLN based architectural refinement, continued pre training, and dual signal distillation from logits and multi head attention relations. Reported […]
The post Microsoft AI Proposes BitNet Distillation (BitDistill): A Lightweight Pipeline that Delivers up to 10x Memory Savings and about 2.65x CPU Speedup appeared first on MarkTechPost.
Kong has open-sourced Volcano, a TypeScript SDK that composes multi-step agent workflows across multiple LLM providers with native Model Context Protocol (MCP) tool use. The release coincides with broader MCP capabilities in Kong AI Gateway and Konnect, positioning Volcano as the developer SDK in an MCP-governed control plane. What Volcano provides? Volcano exposes a compact, […]
The post Kong Releases Volcano: A TypeScript, MCP-native SDK for Building Production Ready AI Agents with LLM Reasoning and Real-World actions appeared first on MarkTechPost.
Are your LLM code benchmarks actually rejecting wrong-complexity solutions and interactive-protocol violations, or are they passing under-specified unit tests? A team of researchers from UCSD, NYU, University of Washington, Princeton University, Canyon Crest Academy, OpenAI, UC Berkeley, MIT, University of Waterloo, and Sentient Labs introduce AutoCode, a new AI framework that lets LLMs create and […]
The post AutoCode: A New AI Framework that Lets LLMs Create and Verify Competitive Programming Problems, Mirroring the Workflow of Human Problem Setters appeared first on MarkTechPost.
Reinforcement Learning RL post-training is now a major lever for reasoning-centric LLMs, but unlike pre-training, it hasn’t had predictive scaling rules. Teams pour tens of thousands of GPU-hours into runs without a principled way to estimate whether a recipe will keep improving with more compute. A new research from Meta, UT Austin, UCL, Berkeley, Harvard, […]
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In this tutorial, we build a compact, efficient framework that demonstrates how to convert tool documentation into standardized, callable interfaces, register those tools in a central system, and execute them as part of an automated pipeline. As we move through each stage, we create a simple converter, design mock bioinformatics tools, organize them into a […]
The post A Coding Implementation to Build a Unified Tool Orchestration Framework from Documentation to Automated Pipelines appeared first on MarkTechPost.
How do you convert complex, multilingual documents—dense layouts, small scripts, formulas, charts, and handwriting—into faithful structured Markdown/JSON with state-of-the-art accuracy while keeping inference latency and memory low enough for real deployments?Baidu’s PaddlePaddle group has released PaddleOCR-VL, a 0.9B-parameter vision-language model designed for end-to-end document parsing across text, tables, formulas, charts, and handwriting. The core model […]
The post Baidu’s PaddlePaddle Team Releases PaddleOCR-VL (0.9B): a NaViT-style + ERNIE-4.5-0.3B VLM Targeting End-to-End Multilingual Document Parsing appeared first on MarkTechPost.
A team of researchers from Google Research, Google DeepMind, and Yale released C2S-Scale 27B, a 27B parameter foundation model for single-cell analysis built on Gemma-2. The model formalizes single-cell RNA-seq (scRNA-seq) profiles as ‘cell sentences‘, ordered lists of gene symbol, so that a language model can natively parse and reason over cellular states. Beyond benchmarking […]
The post Google AI Releases C2S-Scale 27B Model that Translate Complex Single-Cell Gene Expression Data into ‘cell sentences’ that LLMs can Understand appeared first on MarkTechPost.
Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over […]
The post Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of AI Agents appeared first on MarkTechPost.
In this tutorial, we build an AI-powered cryptographic agent system that combines the strength of classical encryption with adaptive intelligence. We design agents capable of performing hybrid encryption with RSA and AES, generating digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous agents securely establish […]
The post A Coding Guide to Build an AI-Powered Cryptographic Agent System with Hybrid Encryption, Digital Signatures, and Adaptive Security Intelligence appeared first on MarkTechPost.
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