Steering Under Compression: Dose-Response, Capability Cost, and Failure Asymmetry in Quantized LLMs
- URL: http://arxiv.org/abs/2609.06473v1
- Date: Sun, 06 Sep 2026 08:41:42 GMT
- Title: Steering Under Compression: Dose-Response, Capability Cost, and Failure Asymmetry in Quantized LLMs
- Abstract summary: Inference-time steering enables behavioral control of large language models without parameter modification.<n>Post-training quantization reduces memory and compute costs for deployment.<n>We systematically study activation steering under weight-only quantization.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization reduces memory and compute costs for deployment. Despite their growing convergence in practice, the interaction between these two techniques remains uncharacterized. We systematically study activation steering under weight-only quantization (INT8 and NF4) across four open-weight 7-9B models and two behavioral targets: judged sentiment and judge-free reasoning length. Using an iso-effect framework that compares capability costs at matched behavioral effect, we find that sentiment steering survives quantization intact. After correcting a GSM8K parser artifact with a uniform v2.3.1 rescore, the pooled INT8 contrast is -0.010 (90% CI [-0.026, +0.007]), descriptively Equivalent under the preregistered three-label rule, while NF4 remains Inconclusive at -0.017 ([-0.067, +0.033]). In contrast, reasoning length exhibits a surprising asymmetric dose-response: lengthening is graded but terminates in cap-runaway and collapse, while shortening is a step function with only 12-30% shortening (model-dependent) before discontinuous failure. We expose a methodological pitfall: the naive iso-effect ladder anchors on the collapse floor for floor-bounded targets, and we introduce a censored construction that restores interpretable crossings. We also quantify a substantial baseline capability shift for Mistral-NF4 (0.545 to 0.365 GSM8K at alpha=0), demonstrating that compression can dominate the steering intervention. Despite this, steering vectors remain highly collinear with their FP16 siblings (cosine similarity 0.989-0.998 for INT8, 0.945-0.990 for NF4), confirming that the behavioral direction survives quantization even when the cost structure does not. All code and data are released.
Related papers
- GAPS: Dimension-Level Gates for Conditional Activation Steering [5.5832442227189425]
Activation steering suppresses undesired behaviors in language models by adding a steering vector to the hidden state during generation.<n>Recent conditional methods such as CAST and DSAS improve the behavior-capability trade-off by deciding when to intervene, but once active, they apply the full dense vector to all hidden dimensions.<n>We introduce dimension-level conditioning as a complementary axis of selectivity that also decides which neurons to intervene on.
arXiv Detail & Related papers (2026-09-01T21:21:52Z) - On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability [57.03393882933515]
We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and 51B parameters of n-gram embedding tables held off the accelerator.<n>On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs.
arXiv Detail & Related papers (2026-08-31T06:35:07Z) - SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models [53.86918766240095]
Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation.<n>We propose textbfState Anomaly Neutralization (SANE), which applies adaptive $tanh$ compression at chunk boundaries while preserving the intra-chunk parallel structure.
arXiv Detail & Related papers (2026-08-23T10:41:07Z) - Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs [0.23438564092609357]
Proactive interference (PI) is a documented failure mode in large language models in which retrieval of a repeatedly overwritten value degrades as prior overwrites accumulate.<n>PTQ is now the default deployment path for open-weight models, yet its effect on this failure mode has not been tested.<n>We evaluate three precision levels (FP16, INT8, INT4/NF4, via bitsandbytes) across three architecturally distinct instruction-tuned models.
arXiv Detail & Related papers (2026-08-19T06:17:13Z) - Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk [0.0]
Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters.<n>A common proposal closes that gap with a language model: compute feature attributions, hand them to an LLM, and let it write the rationale.<n>We build such a system end to end and test whether the second half of the promise holds.
arXiv Detail & Related papers (2026-08-08T13:22:14Z) - Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts [0.0]
We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved.<n>We classify 30,000 chain-of-thought outputs from five instruction-tuned LLMs across three quantization precisions.<n>We find that while accuracy is robust across precisions, Hollow Convergence shows a significant size-dependent shift under NF4.
arXiv Detail & Related papers (2026-07-10T21:55:05Z) - Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning [55.264863369127774]
Current methods reduce each correct rollout to a single reward bit, ignoring the geometric structure shared among their hidden states.<n>We propose Hidden-Align, an auxiliary loss function that aligns the last-layer hidden states of correct rollouts at the anchor token during RL training, with zero overhead in both training and inference.<n>On eight mathematical reasoning benchmarks, Hidden-Align improves average pass@1 over the DAPO baseline by 3.8, 6.2, and 5.4 percentage points on Qwen3-1.7B, 4B, and 14B respectively, with consistent pass@k gains across all three scales.
arXiv Detail & Related papers (2026-06-02T06:51:15Z) - Perplexity Can Miss SAE Feature Damage Under Quantization [0.16921396880325779]
Quantized models are judged acceptable when perplexity or downstream accuracy remains close to the full-precision original.<n>We test this by using a frozen sparse-autoencoder (SAE) as a fixed measurement basis.
arXiv Detail & Related papers (2026-06-02T01:17:05Z) - ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models [45.62029693245481]
We introduce ActQuant, an action-guided mixed-precision PTQ framework.<n>ActQuant operates in two stages: (1) an inter-tensor bit allocator that assigns each weight matrix a single bitwidth based on how much it contributes to predicting the agent's actions.<n>We also introduce OmniModel, an agentic conversion pipeline that ports into a native C/C++ with efficient low-bit kernels.
arXiv Detail & Related papers (2026-05-19T19:57:26Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - When Flat Minima Fail: Characterizing INT4 Quantization Collapse After FP32 Convergence [0.0]
Post-training quantization assumes that a well-converged model is a quantization-ready model.<n>We show this assumption fails in a structured, measurable, and previously uncharacterized way.
arXiv Detail & Related papers (2026-04-16T15:46:36Z) - Quantile Advantage Estimation for Entropy-Safe Reasoning [44.192277495613695]
Reinforcement Learning with Verifiable Rewards (RLVR) strengthens LLM reasoning, but training often oscillates between entropy collapse and entropy explosion<n>We trace both hazards to the mean baseline used in value-free RL, which improperly penalizes negative-advantage samples under reward outliers.<n>We propose Quantile Advantage Estimation (QAE), replacing the mean with a group-wise K-quantile baseline.
arXiv Detail & Related papers (2025-09-26T17:37:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.