Aug 2026· 1 citation· ⚡ 1 influential· 27 references
Computer Science
TL;DR
Right Reset (RR) is introduced and an observed-token likelihood-ratio readout is competitive in some architectures, indicating that the central contribution is the intervention: context dependence itself can provide a boundary signal when surface structure is weak.
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. We turn this observation into prefix-removal probing and introduce Right Reset (RR), which measures preservation of the right-hand hidden-state trajectory. A dynamic program converts RR edge scores into variable-length chunks. On flattened text formed by concatenating topically similar records after deleting their separators and layout, RR recovers 47.7% of the original records as clean units, versus 25.9% for a BGE embedding boundary, the strongest tested conventional baseline without task-specific model training. The gain persists after rendering and OCR. Passive scores from the same Qwen3-4B layer and direct prompting of a same-scale instruction model perform substantially worse on flattened records. Across six language models, RR-selected cuts also undergo consistently less local output disruption than unselected candidate edges. An observed-token likelihood-ratio readout is competitive in some architectures, indicating that the central contribution is the intervention: context dependence itself can provide a boundary signal when surface structure is weak.
Exact deletion from persistent language-model memory depends on whether a record's effect remains addressable after later computation. Native Kimi Delta Attention (KDA) gives a negative result for the tested receipt interface: the corpus-pooled raw recurrent contribution changes by 12-49% with the suffix and remains 8-49% after a decay-ledger correction. Native omission also changes later transition and write terms and other active caches. Frozen-input transport succeeds on its fixed-input control; the changed terms place native omission outside the tested receipt classes. Checkpoint replay supplies the evaluated recomputation path; zero residual on final logits and all 80 audited KDA arrays verifies restoration across the declared checkpoint surface. The complementary result is constructive. We retrofit support-vector memory into frozen Gemma 3 without attention transfer, low-rank recovery, distillation, adapters, or language-model parameter updates. Prefix-mass preservation and one box per prefix solve give base-matched admission at 4B with 1.85% perplexity overhead. At 1B and 4B, verified deletion agrees with its conditional retained-key refit within 1.3e-10 maximum next-token KL; behavioral attacks at 4B reach never-stored or chance baselines. Across 1B, 4B, and 12B, the 4B checkpoint uniquely combines base-matched admission with low overhead. The paper's two contributions are a negative result for native KDA's tested receipt classes and a positive training-free construction for addressable pretrained memory.
This work studies a production policy in which each ordered family is the conjunction of two regular-language predicates, and shows that the resulting monitor is release-boundary equivalent to an absorbing cumulative oracle for every policy in the declared grammar.
It is concluded that mean attention degradation is largely descriptive rather than prescriptive: function tokens contribute through what their hidden states compute, not through the attention they receive -- with implications for interpretability methodology and attention-score-based inference optimisations such as KV-cache eviction.
Hard prompt compression reduces long-context inference cost by independently scoring tokens, sentences, or chunks and retaining the highest-scoring units under a budget. We identify a structural failure in this procedure: independent selection can split dependent evidence pairs, retaining one member while deleting the other. When retained text contains an answer but deleted text defines the entity needed to interpret it, we call the result referential dangling. At a compression ratio of 0.30, Beaver, which ranks coherent chunks using Qwen3-0.6B embeddings, leaves the answer path incomplete in 34-54% of bridge examples across three multi-hop question answering datasets. On a shared HotpotQA bridge set, all six hard compressors we test exhibit dangling at rates up to 60%, and every document in LongBench-v2 Single-Document QA contains at least one dangling reference. On dangling examples evaluated with Qwen3-8B, reinserting the missing supporting paragraph while removing nonsupporting paragraphs to maintain the token budget improves accuracy by 29-34 percentage points (p<0.0001), recovering at least 88% of the gap to contexts retaining both supporting paragraphs. Stronger answer models do not absorb the loss: on MuSiQue, GPT-5.5 is 8.8 points less accurate on compressed contexts than on contexts retaining both supporting paragraphs. Finally, we train a compact classifier to rank omitted sentences by whether they are needed to interpret retained text and reinsert the top-ranked candidates without support annotations at inference. On HotpotQA with Qwen3-8B, this automatic restoration improves accuracy by 4.7 points while changing the compression ratio only from 0.30 to 0.31. Hard compressors should optimize both relevance and referential completeness.
These results support depthwise convolution as a lightweight complement to self-attention for modeling short-range token interactions and suggest that the convolution makes repeated token IDs more sensitive to their immediate context.
Yuchuan Tian, Yingte Shu, Wei He et al.· 0 citations
Attention Diversion explains why inference-time strategies, including chain-of-thought prompting, spell-checking, self-repair, and stronger repair models, fail to consistently recover performance: each addresses one channel at a time.
Jiaqi Zhu, Yang Zhang, Junhua Ding et al.· 0 citations