6arXiv cs.CL (Computation and Language)·
Associative Recurrent Memory Transformer enables long-context LLMs with constant memory scaling
Researchers propose using the Associative Recurrent Memory Transformer (ARMT) as a practical method for extending LLM context length beyond original limits while achieving constant memory scaling instead of linear. The training recipe combines continued pre-training, synthetic long-context data, curriculum learning, and selective layer integration of associative memory. Experiments show ARMT-augmented models generalize to out-of-distribution context lengths and require 30% fewer FLOPs while preserving in-window baseline performance.
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Randomized YaRN improves LLM length generalization for long-context reasoning
Researchers propose Randomized YaRN, a training method that combines YaRN-based positional extrapolation with randomized positional encodings and a length curriculum to improve LLM generalization to long contexts. Models trained on sequences under 8K tokens show consistent reasoning improvements on context lengths from 16K to 128K on BABILong and MRCR benchmarks. The key insight is that exposing models to out-of-distribution positional representations during short-context training enables better generalization at far longer inference-time lengths.
AutoMem: Automated framework trains LLMs to manage memory as a learnable cognitive skill
AutoMem is a new framework that treats memory management in LLMs as a trainable skill, using two optimization loops: one that iteratively revises memory structure via trajectory review by a strong LLM, and one that distills good memory decisions into direct training signal for the agent model. Evaluated on three long-horizon procedurally generated games (Crafter, MiniHack, NetHack), optimizing memory alone yielded 2x-4x performance improvements, bringing a 32B open-weight model competitive with frontier systems like Claude Opus 4.5 and Gemini 3.1 Pro Thinking. The work draws on cognitive science concepts of metamemory and demonstrates that memory management is an independently learnable, high-leverage capability for long-horizon agentic tasks.
Language Models Need Sleep: Periodic Context Consolidation via Fast Weights and SSM Blocks
This paper proposes a sleep-like consolidation mechanism for transformer-based LLMs to address the quadratic scaling of attention with context length. During 'sleep' phases, the model performs N offline recurrent passes over accumulated context, updating fast weights in state-space model (SSM) blocks via a learned local rule, then clears the KV cache. The approach is evaluated on synthetic tasks (cellular automata, multi-hop graph retrieval) and math reasoning, where standard transformers and SSM-attention hybrids fail, with performance scaling with sleep duration N.
Sleep paradigm for LLMs enables continual learning and memory consolidation via distillation and RL
A new arXiv preprint proposes a 'Sleep' paradigm for language models that enables continual learning by consolidating short-term in-context memories into long-term parameters. The framework has two stages: Knowledge Seeding (distilling a smaller model's memories into a larger network via on-policy distillation combined with RL-based imitation learning) and Dreaming (self-improvement via RL-generated synthetic curricula without human supervision). Experiments cover long-horizon tasks, continual learning, knowledge incorporation, and few-shot generalization, addressing a known weakness of current LLMs in retaining temporal knowledge across contexts.
ReContext: Training-free recursive evidence replay improves LLM long-context reasoning
Researchers introduce RECONTEXT, a training-free inference-time method for improving long-context reasoning in LLMs. The approach uses model-internal relevance signals to build a query-conditioned evidence pool that is replayed before final generation, without modifying the original context, external memory, or context pruning. Experiments across eight long-context datasets at 128K context length show consistent improvements on Qwen3-4B, Qwen3-8B, and Llama3-8B. The authors provide a theoretical grounding via associative memory theory, framing attention as cue-trace association and replay as trace reactivation.
A Sleep-Like Consolidation Mechanism for LLMs
A preprint on arXiv proposes a sleep-like memory consolidation mechanism for large language models, drawing an analogy to biological sleep-based memory consolidation in neural systems. The work appears to address how LLMs might better retain and integrate new information over time, a key challenge in continual learning and knowledge updating. The paper attracted notable community attention on Hacker News with 164 points and 122 comments, suggesting broad interest in the approach.
Test-Time Training End-to-End (TTT-E2E) Retrains Model Weights to Handle Long Inputs
Researchers from Astera Institute, Nvidia, Stanford, UC Berkeley, and UC San Diego introduced TTT-E2E, a method that compresses long context into transformer weights by training the model during inference via meta-learning. The approach uses sliding-window attention restricted to 8,000 tokens and updates only the fully connected layers of the last quarter of the network on each 1,000-token chunk at inference time, keeping per-token generation latency roughly constant as context scales to 128,000 tokens. TTT-E2E slightly outperforms vanilla transformers on next-token prediction loss across long contexts and matches efficient architectures like Mamba 2 and Gated DeltaNet on inference speed, but fails dramatically on Needle-in-a-Haystack retrieval beyond 8,000 tokens and incurs substantially higher training latency. The work reframes long-context handling as a training-inference trade-off rather than an architectural design problem.
Co-LMLM: Continuous-query limited memory language models outperform vanilla LLMs on factual tasks at small scale
Researchers introduce CO-LMLM, a limited memory language model that externalizes factual knowledge to a knowledge base during pretraining and retrieves it at inference via continuous vector queries paired with human-readable text values. The approach removes prior restrictions to relational knowledge bases and Wikipedia-only data by introducing an annotation pipeline for arbitrary text. At 360M parameters, CO-LMLM achieves lower perplexity than models trained on 40x more data and SimpleQA factual performance comparable to GPT-4o mini and above Claude Sonnet 4.5, suggesting significant efficiency gains for factual grounding.