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Chain-of-Agents: Redefining LLM Reasoning with Agent Foundation Models
Chain-of-Agents: Redefining LLM Reasoning with Agent Foundation Models
The Chain-of-Agents (CoA) approach introduces a bold new paradigm: encoding multi-agent problem solving within a single model. Instead of relying on external orchestration frameworks, CoA enables dynamic handling of multiple roles—planner, reflector, verifier, web tool, code executor—all in one end-to-end model.
A key breakthrough: CoA reduces inference cost by 84.6% compared to traditional multi-agent systems, while simultaneously improving state-of-the-art performance across reasoning, coding, and web agent benchmarks.
Core Innovations
Multi-Agent Distillation (CoA Distillation):
Trains the model using trajectories sourced from state-of-the-art multi-agent systems, consolidating agent behaviours into coherent single-model workflows.
Agentic Reinforcement Learning:
Refines the model using RL on verifiable tasks where outcomes like code execution or web-based answers are objectively scored.
Mask Fine-Tuning & Tool Calling Support:
Offers selective learning and integrated tool usage—web search, crawling, secure code sandboxing—within the model pipeline.
Performance Highlights:
Using Qwen-2.5 as the base, the AFM (Agent Foundation Model) delivers state-of-the-art results:
> GAIA benchmark average success rate (Pass@1): 55.3%, scaling up to 57.3% (test-time sampling) and 69.9% at Pass@3.
> WebWalker: 63.0% base, rising to 64.7%.
> BrowseComp and HLE show significant improvements—even the 7B model yields competitive results like 15.6% on HLE.
Extensive Open-Source Ecosystem:
> Full model weights (7B & 32B variants for web, code, and multi-hop QA agents, in both SFT and RL flavours)
> All training and inference code, datasets, and technical documentation, under an Apache-2.0 license—for full reproducibility and extension.
From a research perspective, it’s an invitation to explore and build upon a unified architecture that encapsulates planning, reflection, tool integration, and verification within one model—breaking reliance on brittle engineering or costly, agent-based orchestration.
For practitioners and product teams, it’s a performance leap with practical upside: one model that does multi-tool reasoning with dramatically improved efficiency and scalability, ready to be adapted—for example, into AI copilots, intelligent web agents, or coding assistants.
Paper link:https://www.arxiv.org/pdf/2508.13167
#AI #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #FoundationModels #MultiAgentSystems #ReinforcementLearning #AIResearch #AIAgents #ReasoningModels #OpenSourceAI #DataScience #MLOps #AIEngineering #IntelligentAgents
Quantifying uncertainty in LLMs
Quantifying uncertainty in LLMs
Not all model outputs are created equal. Exploring a great tool for quantifying uncertainty in LLMs.
As we integrate Large Language Models (LLMs) into more critical applications, understanding when to trust their output is just as important as the output itself. This is where Uncertainty Quantification (UQ) becomes essential.
Open-source library called uQLM (Uncertainty Quantification for Language Models) that tackles this exact problem.
It provides a comprehensive toolkit to measure an LLM's confidence in its own generations, going beyond a simple softmax probability. The repo includes implementations for several state-of-the-art methods:
> Semantic entropy for detecting uncertainty in free-form generations
> Pragmatic entropy and Pragmatic calibration
> Ensemble-based methods
This is crucial for building reliable AI systems in high-stakes fields like healthcare, finance, or legal, where a model's miscalibrated confidence can lead to significant errors.
By quantifying doubt, we can build safeguards, trigger human review, and ultimately create more trustworthy and responsible AI.
Major kudos to the AI team at CVS Health team for open-sourcing this valuable contribution to the ML community. It's a concrete step towards more robust and transparent AI.
From General LLM to Domain Master: Crafting Specialised Intelligence
From General LLM to Domain Master: Crafting Specialised Intelligence
Open-source LLMs are powerful—but their real value lies in how well they adapt to your data, your structure, and your goals.
I recently fine-tuned a Mistral 7B model on a highly domain-specific dataset—not just for fluency, but for precision, structure, and consistency. The result? A model that delivers predictable, production-grade outputs.
Why Fine-Tune When Models Are Already “Smart”?
Even strong instruction-tuned models can fall short when:
Data is dense or technical: Complex jargon and high redundancy.
Output must follow strict formats: JSON, XML, or schema-based structures.
Reliability beats creativity: In closed-loop systems, structure is everything.
Behind the Scenes
Data Prep: Chunked long docs with overlap; stored efficiently in JSONL.
Hugging Face Stack: Used transformers, datasets, and accelerate.
Efficient Training: bfloat16 precision, 5 epochs, batch size optimised for around a couple of GPU.
What Stood Out
Consistency: Predictable, structured outputs—no hallucinations.
Adaptability: Maintained instruction-following strength post-tuning.
Prompting: Template-based prompts during training improved formatting and accuracy.
Challenges Faced
Memory constraints: Careful batching and precision settings.
Overfitting: Handled with early stopping and validation.
Evaluation: Structured output scoring (e.g., JSON) requires custom metrics.
Why This Matters
This isn’t just about summarization—it’s about powering workflows that demand precision: legal automation, compliance-ready docs, structured financial or scientific outputs.
If your systems need structured, reliable LLM outputs, fine-tuning isn’t optional—it’s essential.
Are you adapting open models for structured production use? Let’s connect.
LazyGraphRAG: Redefining Performance and Efficiency in RAG
LazyGraphRAG: Redefining Performance and Efficiency in RAG
Traditional RAG systems, while powerful, often face challenges such as high computational cost and suboptimal retrieval accuracy.
LazyGraphRAG, Microsoft’s groundbreaking graph-based approach that addresses these limitations head-on, delivering improved performance and cost-effectiveness.
As per Microsoft, what sets LazyGraphRAG apart?
1️⃣ Improved Relevance: Traditional RAG methods rely on exhaustive retrieval, often fetching redundant or irrelevant documents. LazyGraphRAG uses graph-based lazy evaluation, focusing only on the most relevant nodes in the graph, ensuring better context for generation.
2️⃣ Superior Efficiency: By avoiding unnecessary retrieval operations, LazyGraphRAG reduces computational overhead. Compared to traditional RAG systems, it achieves up to 60% lower inference costs without compromising quality.
3️⃣ Enhanced Scalability: While traditional RAG systems struggle with scaling to large document corpora, LazyGraphRAG leverages its graph structure to scale seamlessly, making it ideal for enterprise-grade applications.
4️⃣ State-of-the-Art Performance : LazyGraphRAG outperforms traditional RAG methods on benchmarks for accuracy and relevance. Its ability to retrieve precise and contextually appropriate information significantly improves the quality of responses.
This innovation can prove to be a game-changer for businesses that rely on context-rich, cost-efficient AI systems for knowledge management, customer support, and more.
Explore how LazyGraphRAG can transform the AI strategy:LagyGraphRAG
LLM Pruning and Distillation in Practice: The Minitron Approach
LLM Pruning and Distillation in Practice: The Minitron Approach
Nvidia published a paper, recently, titled "LLM Pruning and Distillation in Practice: The Minitron Approach".
Here summary of the paper:
- The paper presents methods for compressing large language models (LLMs) using pruning and distillation techniques, specifically focusing on Llama 3.1 8B and Mistral NeMo 12B models, compressing them to 4B and 8B parameters respectively.
- Two pruning strategies are explored: depth pruning (removing entire layers) and width pruning (compressing neurons, attention heads, etc.).
- Teacher models are fine-tuned on the distillation dataset before applying pruning, which is crucial for optimal performance.
- The resulting models, Llama-3.1-Minitron-4B and MN-Minitron-8B, demonstrate strong performance on common benchmarks with significantly fewer training tokens.
- The MN-Minitron-8B model outperforms the teacher on some benchmarks, and the pruned models offer substantial improvements in runtime performance, with speedups of up to 2.7× compared to the original models.
- The paper opens sources these compressed models on Hugging Face for wider accessibility.
So what is LLM Pruning and Distillation?
LLM Pruning:
Definition: Pruning in large language models (LLMs) is a technique used to reduce the size of the model by removing less important parts, such as neurons, layers, or attention heads.
Purpose: The goal is to make the model more efficient by reducing the number of parameters, which can decrease memory usage and increase inference speed, without significantly impacting performance.
Types of Pruning:
- Depth Pruning: Involves removing entire layers from the model.
- Width Pruning: Involves compressing within layers by reducing the number of neurons, attention heads, or other components.
Importance Estimation: Before pruning, the importance of each component (e.g., layer, neuron) is evaluated based on their contribution to the model's performance. Less important components are removed.
Result: Pruning leads to a smaller, more efficient model that maintains most of the original model's performance.
LLM Distillation:
Definition: Distillation is a process where a smaller model (student) learns to mimic a larger, more complex model (teacher). The student model is trained to replicate the output of the teacher model.
Purpose: The goal is to create a smaller, faster model that retains the performance characteristics of the larger model.
Process:
Teacher Correction: If the original training data is unavailable, the teacher model may be fine-tuned on a new dataset to better align with the distillation process.
Knowledge Transfer: The student model is trained using the output of the teacher model, often by minimizing a loss function like KL divergence between the teacher and student logits (probabilities).
Result: The distilled model is smaller and more efficient but aims to maintain similar performance levels to the original larger model.
Link to paper:https://arxiv.org/pdf/2408.11796
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