AI Research Papers
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ARE: scaling up agent environments and evaluations
Meta AI Researcher Explains ARE and Gaia2: Scaling Up Agent Environments and Evaluations
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AgentArch: Benchmarking AI Agents for Enterprise Workflows
ServiceNow’s Tara Bogavelli on AgentArch: Benchmarking AI Agents for Enterprise Workflows
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Small Language Models are the Future of Agentic AI
NVIDIA’s Peter Belcak distills why small language models are the future of agentic AI.
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Verizon’s Stan Miasnikov Walks Through His Latest Paper On Inter-Agent Communication
Presents an extension of a recursive consciousness framework to analyze communication between agents and the inevitable loss of meaning in translation
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A Watermark for Large Language Models
Proposed LLM watermark that can be embedded with negligible impact on text quality.
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Self-Adapting Language Models
A novel method for enabling LLMs to adapt their own weights using self-generated data and training directives
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Accurate KV Cache Quantization with Outlier Tokens Tracing
Researchers propose a smarter way to compress the KV Cache while preserving model quality.
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Scalable Chain of Thoughts via Elastic Reasoning
Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models (LRMs) by explicitly separating the reasoning process into two distinct phases: thinking and solution.
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Sleep-time Compute: Beyond Inference Scaling at Test-time
We recently discussed “Sleep Time Compute: Beyond Inference Scaling at Test Time,” new research from the team at Letta.
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Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies
LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of large language models?
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Agent-as-a-Judge: Evaluate Agents with Agents
This week we dive into a paper that presents the “Agent-as-a-Judge” framework, a new paradigm for evaluating agent systems.
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Introduction to OpenAI’s Realtime API
We break down OpenAI’s realtime API. Sally-Ann DeLucia and Aparna Dhinakaran cover how to seamlessly integrate powerful language models into your applications for instant, context-aware responses that drive user engagement.
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Model Context Protocol (MCP) from Anthropic
Want to learn more about Anthropic’s groundbreaking Model Context Protocol (MCP)? We break down how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents.
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How DeepSeek is Pushing the Boundaries of AI Development
How do you train an AI model to think more like a human? That’s the challenge DeepSeek is tackling with its latest models, which push the boundaries of reasoning and reinforcement learning.
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Multiagent Finetuning: A Conversation with Researcher Yilun Du
This week we were excited to talk to Google DeepMind Senior Research Scientist (and incoming Assistant Professor at Harvard), Yilun Du, about his latest paper “Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains.”
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Swarm: OpenAI’s Experimental Approach to Multi-Agent Systems
As multi-agent systems grow in importance for fields ranging from customer support to autonomous decision-making, OpenAI has introduced Swarm, an experimental framework that simplifies the process of building and managing these systems.
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Breaking Down Reflection Tuning: Enhancing LLM Performance with Self-Learning
A recent announcement on X boasted a tuned model with pretty outstanding performance, and claimed these results were achieved through reflection tuning.
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Top AI research papers
| Source | Description | ||
|---|---|---|---|
| Source | Why Language Models Hallucinate | Description |
Shows the math and evaluation choices that underlie LLM hallucinations. |
| Source | ARE and Gaia2 | Description |
Gaia2 is a new AI agent benchmark that checks write actions — the ones that modify the world (like sending an email) — and don’t explicitly verify pure reads. |
