6arXiv cs.CL (Computation and Language)·
Prompt compression via learned activation aggregation reduces per-query compute with under 2% accuracy drop
A new arXiv preprint proposes compressing instruction prompts into a single activation vector by taking a learned weighted sum of intermediate-layer activations and re-injecting it at an early layer of the target LLM. The method achieves under 2% accuracy degradation relative to full prompt processing, eliminating the need to reprocess fixed instruction prompts on every query. The work also surfaces structural findings about LLM activation spaces: mid-layer representations transfer to early layers, and a single vector can encode recoverable semantic information.
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Early abort cascade for LLM agents predicts episode failure from hidden activations, saving ~47% inference compute
A new arXiv paper introduces a method to detect doomed LLM agent episodes early by probing internal hidden-state activations, rather than waiting for observable failure. The approach uses a cascade of calibrated per-round gates with recall budgets, guaranteeing that eventually-successful episodes survive at a user-specified rate. On TextCraft with Qwen-2.5-7B and Llama-3.2-3B, the cascade saves 37–47% of inference compute at a 90% recall target, outperforming behavior-only baselines by roughly 2x. The work provides both a practical deployment mechanism and theoretical guidance on sample complexity for certifying high recall targets.
DepthWeave-KV: Token-adaptive cross-layer KV cache compression for long-context inference
A new arXiv preprint introduces DepthWeave-KV, a KV cache compression method that factorizes key-value states across neighboring transformer layers using shared low-rank channel bases while retaining token-specific residuals for attention-sensitive positions. A token-conditional depth router allocates higher reconstruction rank to instruction-bearing and retrieval-critical tokens, with calibration-free online error tracking during generation. The method achieves 8.3x KV memory reduction at 64K context while maintaining near-full-cache quality on LongBench, Needle-in-a-Haystack, and L-Eval benchmarks. The work addresses a practical bottleneck in long-context inference without requiring base model retraining.
FreqDepthKV: Frequency-guided KV cache compression for long-context LLM inference
A new arXiv preprint introduces FreqDepthKV, an inference-time KV cache compression method that factorizes adjacent-layer KV states into shared low-frequency depth components and sparse high-frequency residuals. A lightweight online probe dynamically assigns attention heads to different caching modes without retraining. On a 32k-token prefill window, the method achieves a 3.9x compression ratio while closely matching full-KV accuracy across QA, retrieval, summarization, and code generation benchmarks, improving decoding throughput to 70.4 tokens/s and reducing peak KV memory to 6.2 GB.
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.
CLP: Lightweight collocation-length predictor achieves zero-loss multi-token inference speedup
Researchers propose CLP (Collocation-Length Predictor), a span-level decision layer for accelerating LLM inference via multi-token prediction without quality degradation. The key insight is 'Backbone-as-Architect': the backbone LM head always generates the first token while MTP heads handle only subsequent tokens, eliminating head-backbone competition that causes repetitive outputs in prior methods. CLP uses a single linear layer (~4.6K–7.7K parameters) versus 1M-parameter gate networks in prior work, achieving 1.14x–1.29x speedup on Qwen2.5 models with near-zero repetition ratio. The paper also establishes that shorter prediction horizons improve MTP head accuracy on larger models, offering a scaling-aware design principle.
Radial suppression of hidden representations accelerates grokking by up to 6x on algorithmic tasks
A new arXiv preprint presents a geometric analysis of the memorization-to-generalization delay (grokking) in neural networks, attributing it to radial inflation of hidden representations under cross-entropy optimization. The authors formalize a radial-angular decomposition of activation-space dynamics and derive three testable propositions about the effects of penalizing radial growth. Empirically, a single-hyperparameter norm penalty that constrains activations to a sqrt(d)-radius hypersphere accelerates grokking up to 6x on modular arithmetic tasks across MLPs and Transformers, and halves training steps for a 10M-parameter nanoGPT on 3-digit addition.
COMPACT-VA: Planning-aligned token compression for long-context autonomous driving
Researchers introduce COMPACT-VA, a working memory framework using conditional VQ-VAE to compress extended temporal context in vision-action autonomous driving models. Compression is conditioned on historical trajectory and a learned planning intent derived from future trajectories during training, enabling end-to-end optimization without backbone modifications. On high-signal dynamic scenarios, the method achieves 68.3% success rate (>6% improvement) with 3.3x speedup and 2.7x memory reduction over uncompressed processing.
Unlocking Longer Generation with Key-Value Cache Quantization
This Hugging Face blog post covers KV cache quantization as a technique to reduce memory consumption during LLM inference, enabling longer context generation without proportional VRAM increases. The post likely explains how quantizing the key-value cache (e.g., to INT8 or lower precision) trades minimal accuracy for significant memory savings. This is directly relevant to inference efficiency and long-context deployment patterns.