LLM

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A novel runtime harness approach improves frozen LLM agents by converting interaction failures into reusable interventions, outperforming model-centric training.

Life-Harness: Adapting the Interface for Deterministic LLM Agents

Introducing Life-Harness, a lifecycle-aware runtime harness that significantly improves frozen LLM agents without modifying model weights. By adapting the interface to convert recurring interaction failures into reusable interventions across various categories, Life-Harness achieved an average 88.5% relative improvement across 116 out of 126 model-environment settings on seven deterministic benchmarks.

Millions invested in LLM alignment are undone by a simple script and electricity costs less than a fast-food meal, exposing a critical flaw in AI safety economics.

The $20 AI De-alignment: How Safety Guardrails Evaporate for Pocket Change

A group called Heretic demonstrated how to strip alignment and censorship from 168 open-weight LLMs for just $20, using "weight surgery." This automated process, which bypasses human judgment, reveals a six-order-of-magnitude cost asymmetry that undermines corporate-scale AI safety investments and highlights performance gains in de-aligned models.

Discover how Duckle's visual builder, 290+ connectors, and local AI assistant streamline your data workflows, replacing heavy ETL and fragile spreadsheets.

Duckle: The Local-First Desktop Data Pipeline Studio You Need

Explore Duckle, a local-first desktop data pipeline studio. Learn about its visual drag-and-drop builder, 290+ connectors, DuckDB integration, and a local AI assistant. Understand its offline capabilities, Git-ready workspaces, and how it simplifies ETL for single-machine workloads.

Discover how a novel framework, inspired by diffusion models, enables training of massive Transformers with significantly reduced memory footprint.

How DiffusionBlocks Overcomes the Deep Learning Memory Wall

Explore the "memory wall" in deep learning and how DiffusionBlocks, by reinterpreting residual networks as diffusion processes, offers a principled, block-wise training method. Learn how it dramatically cuts memory usage for large Transformer models, making them accessible on standard hardware.

Control over AI infrastructure—data, algorithms, and compute power—is the new geopolitical battleground, reshaping global power dynamics and national security.

The AI Arms Race: Nations Battle for Digital Sovereignty

Nations are investing billions to secure AI sovereignty. The US launches a $500B initiative, China promotes open-source AI to set global standards, and India builds a sovereign LLM for its multilingual population. This race for AI dominance defines 21st-century power.

Explore Genspark AI, an open-source Super Agent framework for multi-step task automation, offering local operation, diverse LLM integration, and versatile outputs.

What is Genspark AI and How Does It Work?

Discover Genspark AI, an open-source Super Agent framework that orchestrates multiple LLMs to plan, reason, and execute complex tasks. Learn about its local operation, customizability, and ability to generate dynamic Sparkpages, presentations, spreadsheets, and more, all without subscription costs or vendor lock-in.

Learn to use MLLM-Jailbreak-Bench, a reproducible and model-agnostic framework for measuring harmful output in multimodal large language models.

How to Evaluate Multimodal LLM Safety with MLLM-Jailbreak-Bench

Discover MLLM-Jailbreak-Bench, an evaluation framework for assessing multimodal LLM safety across five attack categories. Understand how to measure Attack Success Rate, refusal quality, and calibration error to identify real safety gaps and avoid false positives. Get started with installation and quick-start instructions.

Discover BES, a novel framework coupling forward evolutionary search with backward goal decomposition to overcome sampling bottlenecks in LLM reasoning.

How Bidirectional Evolutionary Search Improves LLM Self-Improvement

This article explains Bidirectional Evolutionary Search (BES), a new framework that enhances LLM self-improvement by combining evolutionary operators for broader exploration with dense, intermediate feedback from goal decomposition. Learn how BES tackles the limitations of traditional sampling methods like best-of-N and tree search.

A novel arXiv study introduces an offline "sleep" mechanism for Transformer-based language models, improving long-horizon task efficiency without increasing online inference costs.

New LLM "Sleep" Phase Boosts Long-Context Performance

Researchers propose a "sleep" phase for large language models that converts recent context into persistent fast weights, clearing the key-value cache. This innovative approach addresses the attention bottleneck, enabling models to handle long-context tasks efficiently and perform better on complex benchmarks like math reasoning.

Shanghai-based AI firm, backed by Tencent and Alibaba, details M2's MoE architecture and "interleaved thinking," while previewing M3's significant performance gains for ultra-long contexts.

MiniMax Unveils M2 Series, Teases M3 with 9.7x Speedup via Sparse Attention

MiniMax releases a technical report on its M2 model series, featuring a sparse Mixture-of-Experts backbone and innovative "interleaved thinking." The report also previews the upcoming M3 model, which achieves a 9.7x prefilling speedup with MiniMax Sparse Attention (MSA) for 1-million-token sequences, pushing AI efficiency boundaries.

Explore the Diffusion Transformer with Flow Matching that powers high-fidelity 48 kHz audio generation from natural language.

How MOSS-SoundEffect v2.0 Revolutionizes Text-to-Audio Synthesis

Discover MOSS-SoundEffect v2.0, a cutting-edge text-to-audio model using a 1.3B-parameter Diffusion Transformer and Flow Matching for superior sound generation. Learn about its capabilities, multilingual support, and optimal settings for creating diverse audio content.

Explore MiniCPM5-1B, a 1B-parameter LLM designed for on-device deployment, featuring state-of-the-art performance and a unique 'Think'/'No Think' dual-mode chat template.

What is MiniCPM5-1B and How Does Its Dual-Mode Architecture Work?

Discover MiniCPM5-1B, an efficient 1B-parameter causal language model optimized for local and resource-constrained environments. Learn about its Llama-based architecture, impressive 131K context window, and innovative 'Think' and 'No Think' modes that enable it to function as both a fast assistant and a deliberate reasoner from a single checkpoint.