Word Coverage Score (WCS): Measuring Lexical Suppression from LLM Sampling Filters
This paper introduces the Word Coverage Score (WCS), a metric that quantifies how much contextually appropriate low-frequency vocabulary is pruned away by standard sampling strategies (Top-p, Top-k, Min-p) in LLMs. The authors audit open-weight models against human-authored corpora to measure the 'lexical survival rate' of high-information words under typical decoding defaults. Their findings provide quantitative evidence that industry-standard sampling parameters act as unintended censorship mechanisms, suppressing linguistic diversity even when rare words exist within the model's probability distribution. The WCS is proposed as a diagnostic tool for tuning the coherence–lexical-richness trade-off.
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CWE-Trace framework reveals LLM vulnerability detection is calibration without comprehension
Researchers introduce CWE-Trace, a benchmark of 834 manually curated Linux kernel samples across 74 CWEs with strict temporal splits to prevent data contamination, used to evaluate 8 vanilla LLMs and 15 LoRA fine-tuned variants on vulnerability detection. Key findings: data contamination provides no measurable advantage (84% of nominally contaminated samples carry no usable memorization signal), and backbone directional priors dominate fine-tuning — models exhibit stable systematic failure modes that resist correction. The best binary detection score reaches only 52.1% (barely above chance) and exact CWE classification Top-1 accuracy stays below 1.3%, indicating fine-tuning shifts output distributions without instilling genuine security reasoning. The work introduces two diagnostic metrics (Directional Failure Index and Hierarchical Distance and Direction) and concludes that detection capability and security understanding are decoupled in current LLMs.
EmbedFilter: Using the unembedding matrix to suppress high-frequency token noise in LLM text embeddings
Researchers identify that LLM text embeddings over-express high-frequency but semantically uninformative tokens when projected onto vocabulary space, degrading embedding quality. They introduce EmbedFilter, a simple linear transformation that filters out the subspace of the unembedding matrix responsible for writing these tokens into embedding space. The method improves zero-shot performance on text embedding benchmarks across multiple LLM backbones and yields a byproduct of dimensionality reduction without quality loss. Code is publicly released.
Semantic vs. Surface Noise in LLM Agents: 68-Cell Measurement Study with Held-Out Validation
This paper documents an empirical phenomenon across 10 LLMs from 7 architecture families: meaning-bearing perturbations (paraphrase, synonym substitution) cause final-answer inconsistency ~19.69 percentage points more often than presentation-level perturbations (formatting, reordering) of comparable severity, across GSM8K, MATH, and HotpotQA benchmarks. The effect is validated on a held-out 11th model (qwen2.5-14B-Instruct) with 1,800 trajectories. Trace-level analysis supports a 'stealth-divergence' picture where semantic perturbations preserve the first action but induce divergence in intermediate reasoning steps, while two prior mechanism claims are explicitly retracted. The study is notable for its honest reporting of stress-test failures and pre-registered replication.
Systematic study reveals effectiveness-fluency trade-offs in LLM conditioning methods
A new arXiv paper systematically evaluates a range of LLM conditioning methods across both concept injection and removal scenarios, finding that efficient steering methods often degrade fluency significantly. A key finding is that activation steering is substantially less effective on instruction-tuned models than on base models, a previously overlooked interaction. Simple prompting and supervised fine-tuning work for concept injection but not removal, and cheap textual metrics are found to correlate well with expensive LLM-as-judge evaluations.
Real Images, Worse Judgments: Evaluating VLMs on Concreteness and Imagery
This paper evaluates whether vision-language models (VLMs) benefit from real image context when making lexical judgments about word concreteness and imagery. The authors find that real-image contexts frequently hurt alignment with human ratings, especially when visual evidence is least relevant to the word being judged. Probing and canonical correlation analysis reveal that real images cause representational shifts and increased sensitivity to spurious visual cues. Instructing models to focus on text-only content at inference time partially mitigates this degradation.
Predictor-gated bank-wise sparsity recipe for dense-to-sparse LLM upcycling from Qwen2.5-8B
A new arXiv preprint introduces a continual training recipe to convert dense LLMs into channel-sparse models without post-hoc pruning. Starting from a Qwen2.5-8B checkpoint, the method uses a low-rank predictor to gate FFN channel routing, achieving 4x sparsity in FFN intermediate activations via a bank-wise top-k rule at 32K context. The routing module is trained on the main language modeling path, making the resulting sparsity hardware-oriented rather than approximate. The authors also identify and patch a layer-local long-context failure mode on the RULER-CWE benchmark.
ToaST: Tokenization with Split Trees Reduces Token Count by 11%+ Over BPE/WordPiece/UnigramLM
ToaST (Tokenization with Split Trees) is a new subword tokenization method that uses a recursive binary split-tree inference procedure and Integer Programming-based vocabulary selection to directly optimize compression. On English text, ToaST reduces token counts by more than 11% compared to BPE, WordPiece, and UnigramLM at vocabulary sizes of 40,960 and above, effectively extending context length for models using it. In 1.5B parameter LM training experiments, ToaST achieves the highest CORE benchmark score, outperforming baselines by 2.6%–7.6% across 22 tasks. The LP relaxation of the vocabulary selection IP is near-integral in practice, yielding provably near-optimal vocabularies.
Latent Context Language Models (LCLMs) achieve competitive encoder-decoder KV cache compression at scale
Researchers introduce Latent Context Language Models (LCLMs), a family of encoder-decoder compressors that map long token sequences to shorter latent embeddings consumed by a decoder, targeting the KV cache memory bottleneck in long-context inference. The authors conduct architecture search and continually pre-train 0.6B-encoder/4B-decoder models on over 350B tokens at compression ratios of 1:4, 1:8, and 1:16. LCLMs improve the Pareto frontier across general-task performance, compression speed, and peak memory, and are demonstrated as efficient backbones for long-horizon agents that can skim compressed context and expand relevant segments on demand. The work closes a previously noted gap between encoder-decoder approaches and KV cache compression methods on the accuracy-efficiency frontier.