Yuxuan Zhu, David H. Yang, Parisa Shiravani, Mohammad Mohammadi Amiri, Tejaswini Pedapati, Subhajit Chaudhury, Keerthiram Murugesan, Pin-Yu Chen
Key-value (KV) caching is a core mechanism in large language models, where intermediate key and value representations are stored to accelerate autoregressive decoding. As generation length increases, KV cache memory and computation grow linearly, making compression critical for efficient long-output reasoning. Dictionary-based sparse KV compression is appealing because it represents each token's KV states as a sparse combination of learned basis elements (atoms) in a dictionary, preserving token-level structure without enforcing a global low-rank constraint. However, existing dictionary-based methods face two practical limitations during decoding: iterative sparse coding methods such as Orthogonal Matching Pursuit add decoding-time overhead, and static dictionaries may mismatch evolving KV distributions under test-time generation. While adaptive dictionary updates can reduce this mismatch, naive append-only strategies lead to dictionary growth during generation. We propose FAD-KV, a \textbf{F}ast \textbf{A}daptive \textbf{D}ictionary KV caching framework for efficient reasoning decoding. FAD-KV integrates (i) a screening-based sparse coding scheme, where a one-shot Top-\(n\) selection identifies a small candidate pool followed by lightweight coefficient refinement, and (ii) a fixed-size dictionary adaptation mechanism that reuses inactive atoms instead of expanding the dictionary. By controlling both sparse coding cost and adaptive dictionary growth, FAD-KV provides a memory-bounded and computation-efficient KV compression framework for long reasoning workloads. Experiments on reasoning and long-context benchmarks show that FAD-KV achieves a strong quality-efficiency trade-off while avoiding unbounded dictionary growth during decoding.
Ching-Yun Ko International Business Machines, Payel Das, Sihui Dai CapitalOne, Georgios Kollias International Business Machines, Subhajit Chaudhury International Business Machines, Aurelie C. Lozano, Pin-Yu Chen International Business Machines
Reasoning over long contexts remains a major challenge for language models, particularly when solving tasks that require integrating multiple facts in sequence or generalizing to new distributions. We argue that this difficulty stems from a lack of structural inductive bias. Recently, alternative frameworks have been proposed to explicitly encode contexts as ordered memory and perform iterative retrieval to construct reasoning chains. Despite the promising results shown in prior arts, they are still heavily reliant on intermediate chain supervision and fall short in showing emergent reasoning generalization in the presence of hard distractions in reasoning-in-a-haystack tasks. Furthermore, we discover that as the amount of distractions increases, traditional episodic memory reads suffer from ill-conditioning problems, which lead to inaccurate context retrievals. In this work, we formalize the motivation for necessary inductive bias in reasoning-in-a-Haystack tasks, propose inference-time memory update procedures mimicking the “identify and remove unnecessary and unrelated details” in constructively responsive reading, introduce staged training inspired by human conceptual understanding, and finally demonstrate the possibilities and limits of such framework in the weakly supervised scenario.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Abinitha Gourabathina Massachusetts Institute of Technology, Inkit Padhi, Manish Nagireddy IBM Research, Subhajit Chaudhury International Business Machines, Prasanna Sattigeri IBM Research
For Large Language Models (LLMs) to be reliably deployed, models must effectively know when not to answer: abstain. Reasoning models, in particular, have gained attention for impressive performance on complex tasks. However, reasoning models have been shown to have worse abstention abilities. Taking the vulnerabilities of reasoning models into account, we propose our Query Misalignment Framework. Hallucinations resulting in failed abstention can be reinterpreted as LLMs answering the wrong question (rather than answering a question incorrectly). Based on this framework, we develop a new class of state-of-the-art abstention methods called Trace Inversion. First, we generate the reasoning trace of a model. Based on only the trace, we then reconstruct the most likely query that the model responded to. Finally, we compare the initial query with the reconstructed query. Low similarity score between the initial query and reconstructed query suggests that the model likely answered the question incorrectly and is flagged to abstain. Extensive experiments demonstrate that Trace Inversion effectively boosts abstention performance in four frontier LLMs across nine abstention QA datasets, beating competitive baselines in 33 out of 36 settings. The code is available at this https://anonymous.4open.science/r/trace-inversion-08BB/.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
David H. Yang Rensselaer Polytechnic Institute, Yuxuan Zhu, Mohammad Mohammadi Amiri Rensselaer Polytechnic Institute, Keerthiram Murugesan International Business Machines, Tejaswini Pedapati, Subhajit Chaudhury International Business Machines, Pin-Yu Chen International Business Machines
Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During generation, LLMs rely on a key-value (KV) cache for autoregressive decoding. However, the memory footprint of the KV cache grows with output length. Prior work on KV cache optimization mostly focus on compressing the long input context, while retaining the full KV cache for decoding. For tasks requiring long output generation, this leads to increased computational and memory costs. In this paper, we introduce ZoomR, a novel approach that enables LLMs to adaptively compress verbose reasoning thoughts into summaries and uses a dynamic KV cache selection policy that leverages these summaries while also strategically “zooming in” on fine-grained details. By using summary keys as a coarse-grained index during decoding, ZoomR uses the query to retrieve details for only the most important thoughts. This hierarchical strategy significantly reduces memory usage by avoiding full-cache attention at each step. Experiments across math and reasoning tasks show that our approach achieves competitive performance compared to baselines, while reducing inference memory requirements by more than 4 \times. These results demonstrate that a multi-granularity KV selection enables more memory efficient decoding, especially for long output generation.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表