Reasoning Environments, LLM Memory, and Financial Interpretability
How much LLMs memorize, motion-aware video models, and test-time context optimization.
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Welcome to this week’s AI Fridays, where we explore how models learn, reason, and remember. Reasoning Gym brings verifiable, domain-rich RL environments to train logic and math agents. A new study reveals how much GPT models truly memorize, while ATLAS rewrites the rules for test-time memory optimization. MotionSight pushes video motion understanding to new depths, and we dive into mechanistic interpretability techniques to open up the black box of financial LLMs.
Here’s what’s new:
🧠 Reasoning Gym: Procedurally-generated RL environments for verifiable reward-based reasoning across domains like algebra, logic, and geometry.
💼 Beyond the Black Box: Interpretability techniques reveal how LLMs behave—and misbehave—in financial use cases.
🧮 LLM Memorization: GPT-style models store 3.6 bits per parameter and "grok" after maxing out memory.
📚 ATLAS: A context-aware memory module achieving up to 80% improvement on 10M-length inputs.
🎥 MotionSight: Fine-grained motion reasoning in videos using blur prompts, plus the new MotionVid-QA dataset with 87K Q&A pairs.
EASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards (🔗 Read More)
Reasoning Gym introduces a novel library of procedurally-generated reasoning environments that provide infinite, complexity-adjustable training data across multiple domains (algebra, geometry, logic, etc.), enabling continuous evaluation and reinforcement learning of reasoning capabilities with verifiable rewards.
Beyond the Black Box: Interpretability of LLMs in Finance (🔗 Read More)
This pioneering work introduces mechanistic interpretability techniques to demystify LLMs in financial applications, demonstrating through experiments how dissecting model internals can enhance transparency, detect biases, and ensure regulatory compliance while enabling controlled modification of model behavior.
How much do language models memorize? (🔗 Read More)
This paper introduces a novel method to measure language models' memorization capacity, finding that GPT-style models store approximately 3.6 bits per parameter, while demonstrating that models initially memorize data until reaching capacity before transitioning to generalization ("grokking").
ATLAS: Learning to Optimally Memorize the Context at Test Time (🔗 Read More)
ATLAS introduces a novel long-term memory module that optimizes memory based on both current and past tokens, overcoming limitations of traditional Transformers and recurrent models, demonstrating superior performance on long-context tasks with up to 80% accuracy improvement on 10M context lengths.
MotionSight: Boosting Fine-Grained Motion Understanding in Multimodal LLMs (🔗 Read More)
MotionSight introduces a zero-shot method using object-centric visual spotlight and motion blur prompts to enhance multimodal LLMs' fine-grained motion understanding in videos, while also contributing MotionVid-QA, the first large-scale dataset for evaluating video motion comprehension with 40K clips and 87K question-answer pairs.