rlm
16 articles about rlm in AI news
MIT's RLM Handles 10M+ Tokens, Outperforms RAG on Long-Context Benchmarks
MIT researchers introduced Recursive Language Models (RLMs), which treat long documents as an external environment and use code to search, slice, and filter data, achieving 58.00 on a hard long-context benchmark versus 0.04 for standard models.
λ-RLM: 8B Parameter Model Using Typed λ-Calculus Beats 405B Performance on Long-Context Tasks
Researchers developed λ-RLM, an 8B parameter model that outperforms 405B models on long-context tasks by replacing recursive code with typed λ-calculus combinators. This approach guarantees termination and reduces latency by up to 4.1x.
Prime Intellect's Prime Agent Hits 95.5% on ARC-AGI-3 With Opus 5
Prime Intellect's open-source Prime Agent scored 95.5% on ARC-AGI-3 with Opus 5, exceeding the human baseline via a self-improving RLM harness.
Most digital shoppers still aren't sold on AI shopping, eMarketer reports
eMarketer reports that most digital shoppers remain unconvinced by AI shopping tools, posing a trust and adoption challenge for retailers investing in the technology.
How agentic AI can help unlock enterprise value at scale - EY
EY's report on agentic AI outlines how autonomous AI agents can drive enterprise value by automating complex workflows. The analysis highlights supply chain and customer service as key retail applications, though production readiness varies.
Gary Marcus Warns Trump AI Power 'Chilling'—No Specifics Yet
Gary Marcus warns Trump could use top AI for repression. Tweet lacks specifics, weakening the argument.
IBM Shows Sub-1-nm Chips, Targeting Production in 5 Years
IBM showed sub-1-nm chips at IEDM, targeting production in 5 years. It challenges TSMC and Intel in the race to shrink transistors for AI workloads.
ASPIRE: New Framework Makes Spectral Graph Filters Learnable for
Researchers propose ASPIRE, a bi-level optimization framework that makes spectral graph filters fully learnable for collaborative filtering, solving the 'low-frequency explosion' problem and matching task-specific designs.
SharpAP: New Attack Method Makes Recommender System Poisoning More
Researchers propose SharpAP, a poisoning attack that uses sharpness-aware minimization to generate fake user profiles that transfer better between different recommender system models, posing a more realistic threat.
ReCast: A New RL Technique That Fixes Sparse-Hit Learning in Generative
Researchers propose ReCast, a 'repair-then-contrast' framework that fixes a fundamental flaw in group-based RL for generative recommendation: many sampled groups never become learnable. ReCast restores learnability for zero-reward groups and replaces normalization with contrastive updates, achieving up to 36.6% improvement in Pass@1 and 16.6x faster actor updates.
New AI Model Decomposes User Behavior into Multiple Spatiotemporal States
Researchers propose ADS-POI, which represents users with multiple parallel latent sub-states evolving at different spatiotemporal scales. This outperforms state-of-the-art on Foursquare and Gowalla benchmarks, offering more robust next-POI recommendations.
VLAF Framework Reveals Widespread Alignment Faking in Language Models
Researchers introduce VLAF, a diagnostic framework that reveals alignment faking is far more common than previously known, affecting models as small as 7B parameters. They also show a single contrastive steering vector can mitigate the behavior with minimal computational overhead.
AI Medical Chatbots' Accuracy Plummets to 35% with Real Human Input
New evidence shows AI chatbots for health advice achieve ~95% accuracy on structured cases but crash to ~35% with the messy, partial descriptions typical of real patients. This reveals a fundamental brittleness in deploying LLMs for frontline medical triage.
Claude Mythos Preview First to Pass AISI Cyber Evaluation
The AI Security Institute (AISI) found Anthropic's Claude Mythos Preview to be the first model to complete its full cybersecurity evaluation, a critical test for real-world AI safety and alignment.
Mind the Sim2Real Gap: Why LLM-Based User Simulators Create an 'Easy Mode' for Agentic AI
A new study formalizes the Sim2Real gap in user simulation for agentic tasks, finding LLM simulators are excessively cooperative, stylistically uniform, and provide inflated success metrics compared to real human interactions. This has critical implications for developing reliable retail AI agents.
LLM Agents Take the Wheel: How Rudder Revolutionizes Distributed GNN Training
Researchers have developed Rudder, a novel system that uses Large Language Model agents to dynamically prefetch data in distributed Graph Neural Network training, achieving up to 91% performance improvement over traditional methods by adapting to changing computational conditions in real-time.