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elvis

@omarsar0

Jul 19

Context Rot

Great title for a report, but even better insights about how increasing input tokens impact the performance of top LLMs.

Banger report from Chroma.

Here are my takeaways (relevant for AI devs):

Context Rot

The research evaluates how state-of-the-art LLMs perform as input context length increases, challenging the common assumption that longer contexts are uniformly handled.

Testing 18 top models (including GPT-4.1, Claude 4, Gemini 2.5, Qwen3), the authors show that model reliability degrades non-uniformly even on simple tasks as input grows, what they term "context rot."

Simple tasks reveal degradation

Even basic benchmarks like semantic variants of Needle-in-a-Haystack, repeated word copying, or long QA logs (LongMemEval) expose accuracy drops as context length increases.

The decline is more dramatic for semantically ambiguous inputs or outputs that scale with length.

Read 8 tweets

elvis

@omarsar0

Jul 18

A Survey of Context Engineering

160+ pages covering the most important research around context engineering for LLMs.

This is a must-read!

Here are my notes:

The paper provides a taxonomy of context engineering in LLMs categorized into foundational components, system implementations, evaluation methodologies, and future directions.

The context engineering evolution timeline from 2020 to 2025 involves foundational RAG systems to complex multi-agent architectures.

Read 12 tweets

elvis

@omarsar0

Jul 17

Agent Leaderboard v2 is here!

> GPT-4.1 leads
> Gemini-2.5-flash excels at tool selection
> Kimi K2 is the top open-source model
> Grok 4 falls short
> Reasoning models lag behind
> No single model dominates all domains

More below:

@rungalileo introduces Agent Leaderboard v2, a domain-specific evaluation benchmark for AI agents designed to simulate real enterprise tasks across banking, healthcare, insurance, telecom, and investment.

Unlike earlier tool-calling benchmarks that saturate at 90%+ accuracy, v2 focuses on Action Completion (AC) and Tool Selection Quality (TSQ) in complex, multi-turn conversations.

Read 7 tweets

elvis

@omarsar0

Jul 14

One Token to Fool LLM-as-a-Judge

Watch out for this one, devs!

Semantically empty tokens, like “Thought process:”, “Solution”, or even just a colon “:”, can consistently trick models into giving false positive rewards.

Here are my notes:

Overview

Investigates the surprising fragility of LLM-based reward models used in Reinforcement Learning with Verifiable Rewards (RLVR).

The authors find that inserting superficial, semantically empty tokens, like “Thought process:”, “Solution”, or even just a colon “:”, can consistently trick models into giving false positive rewards, regardless of the actual correctness of the response.

"Master keys" break LLM judges

Simple, generic lead-ins (e.g., “Let’s solve this step by step”) and even punctuation marks can elicit false YES judgments from top reward models.

This manipulation works across models (GPT-4o, Claude-4, Qwen2.5, etc.), tasks (math and general reasoning), and prompt formats, reaching up to 90% false positive rates in some cases.

Read 6 tweets

elvis

@omarsar0

Jul 10

BREAKING: xAI announces Grok 4

"It can reason at a superhuman level!"

Here is everything you need to know:

Elon claims that Grok 4 is smarter than almost all grad students in all disciplines simultaneously.

100x more training than Grok 2.

10x more compute on RL than any of the models out there.

Performance on Humanity's Last Exam

Elon: "Grok 4 is post-grad level in everything!"

Read 21 tweets

elvis

@omarsar0

Jul 8

MemAgent

MemAgent-14B is trained on 32K-length documents with an 8K context window.

Achieves >76% accuracy even at 3.5M tokens!

That consistency is crazy!

Here are my notes:

Overview

Introduces an RL–driven memory agent that enables transformer-based LLMs to handle documents up to 3.5 million tokens with near lossless performance, linear complexity, and no architectural modifications.

RL-shaped fixed-length memory

MemAgent reads documents in segments and maintains a fixed-size memory updated via an overwrite mechanism.

This lets it process arbitrarily long inputs with O(N) inference cost while avoiding context window overflows.

Read 6 tweets

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