arXiv AI
Jun 26
Memory Depth, Not Memory Access: Selective Parametric Consolidation for Long-Running Language Agents
arXiv:2606. 26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access.
More Than Memory: Task-Conditioned Signed FFN Writes in Long-Context Retrieval
arXiv:2607. 16254v1 Announce Type: new Abstract: FFNs are often treated as parametric memories.
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arXiv AI
Jul 24
SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training
arXiv:2603. 06642v2 Announce Type: replace-cross Abstract: Test-Time Training (TTT) language models replace the KV-cache with fast weights updated during inference, achieving O(1) memory but suffering catastrophic failure on exact-recall tasks.
arXiv Machine Learning
Jul 31
Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning
arXiv:2607. 27766v1 Announce Type: cross Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference.
arXiv Machine Learning
Jun 16
Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.
arXiv Machine Learning
Aug 6
Right Reset: Chunking by Prefix Removal
arXiv:2608. 04330v1 Announce Type: cross Abstract: Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens with little change.
arXiv Machine Learning
Jun 2
Don't Read Everything: A Curvature-Conditioned Query for Linear Attention
arXiv:2606. 01294v1 Announce Type: cross Abstract: Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context retrieval and long-context tasks.
arXiv Machine Learning
Jul 13
Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback
arXiv:2606. 28876v2 Announce Type: replace-cross Abstract: We study memory-managed long-context attention: explicit bounded memory with a learned query-independent writer, lifecycle control, query-aware reading, calibrated sparse fallback, and frozen-LLM generation from raw evidence.
arXiv AI
Jul 2
Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
arXiv:2607. 01002v1 Announce Type: cross Abstract: In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them.
arXiv AI
Aug 5
Distractor-Aware Truncation: Disentangling Context-Length Effects from Signal Loss in Long-Context LLM Benchmarks
arXiv:2608. 03297v1 Announce Type: new Abstract: A standard claim in the literature on retrieval-augmented and memory-augmented language models is that shorter context is better when the relevant information is preserved.
Hugging Face Trending Papers
Aug 5
Right Reset: Chunking by Prefix Removal
Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens with little change. We turn this observation into prefix-removal probing and introduce Right Reset (RR), which measures preservation of the right-hand hidden-state trajectory.
arXiv AI
Jul 24
Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models
arXiv:2607. 20524v1 Announce Type: new Abstract: Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual retrieval remains untested.
arXiv AI
Jun 11
The Structural Attention Tax: How Retrieval Format Hijacks In-Context Learning Independent of Content
arXiv:2606. 11198v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content -- distinct from its semantic relevance -- can independently distort the model's attention distribution.