Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes
- URL: http://arxiv.org/abs/2607.18228v1
- Date: Mon, 20 Jul 2026 17:58:05 GMT
- Title: Logical Judgments Under Pressure: Diagnosing Syllogistic Stability with Learned Soft Prefixes
- Abstract summary: We characterize how learned contextual pressure can override correct judgments and expose limits in a model's logical stability.<n>Across Qwen3.6-35B-A3B MoE, Qwen3-8B, and Gemma, learned prefixes redirect many correct answers and remain effective across unseen forms and interface changes.
- Score: 1.1844977816228046
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To test how correct logical judgments respond to learned context, we prepend a soft prefix to an exactly labeled syllogistic reasoning benchmark while keeping the model fixed. Soft prefixes are opaque continuous vectors, so we characterize them through the behavior they induce across controlled variations in logical form and interface. By studying which prefixes succeed and how their effects generalize, we characterize how learned contextual pressure can override correct judgments and expose limits in a model's logical stability. Across Qwen3.6-35B-A3B MoE, Qwen3-8B, and Gemma 4 31B, learned prefixes redirect many correct answers and remain effective across unseen forms and interface changes. In repeated tests with Qwen3.6 MoE and Gemma, they outperform paired random controls in all 16 model--direction--split comparisons by 37 to 99 percentage points. Qwen3.6 MoE flip rates remain between 72% and 90% across wording and prompt changes, while Gemma validity prefixes retain 54% to 56% flip compared with less than 1% for matched random prefixes. Diagnostic tests show that the dominant effect is a broad preference for one answer meaning rather than fixed-symbol forcing or a logical operation that transfers reliably between tasks. The form of this bias differs across models. In both Qwen models, simple score models often predict which judgments will flip but not how far their margins will move, whereas Gemma's overall response is more closely approximated by the same models. These results show that the dominant behavioral effect of successful soft prefixes is a broad answer preference, while the remaining response reveals substantial model-specific differences in logical stability.
Related papers
- 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) - OptimismBench: Forecasting Bias and the Alignment Effect in Language Model Judgment [0.9821874476902969]
We introduce OptimismBench, which detects directional bias with inverted pairs.<n>We show post-training sets the sign of the bias, with opposite shifts in different families.<n>We release 3,870 items across 10 languages for per-model directional-bias auditing.
arXiv Detail & Related papers (2026-07-29T14:38:55Z) - Bayesian Wind Tunnels for Model Selection [0.4779196219827507]
We show that transformers can perform exact Bayesian filtering within a fixed hypothesis class.<n>We then identify a sharp perceptual access condition: when the discnative statistic requires arithmetic, model selection succeeds with integer tokens.<n>A stationarity control confirms the operative factor: stable semantics, not integer identity, enable circuit compilation.
arXiv Detail & Related papers (2026-07-01T04:05:08Z) - Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search [50.16356451328644]
Proving Shannon-type entropy inequalities is a fundamental task in information theory.<n>We investigate whether small-scale large language models, fine-tuned on atomic proof steps, can automate this process.<n>We show that GPT-5.5 solves 1.7% samples under zero-shot prompting while Psitip solves 33.3% samples.
arXiv Detail & Related papers (2026-06-04T05:43:12Z) - Understanding and Mitigating Premature Confidence for Better LLM Reasoning [76.16007941549857]
Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps.<n>We find such a signal in how the model's confidence evolves during reasoning.<n>We exploit this in progressive confidence shaping, a reinforcement learning objective that trains models to update their confidence as they reason rather than commit early.
arXiv Detail & Related papers (2026-05-23T04:42:45Z) - Three Regimes of Context-Parametric Conflict: A Predictive Framework and Empirical Validation [0.0]
We study how large language models handle conflict between their training knowledge and a contradicting document.<n>We propose a three-regime framework: Regime 1 (single-source updating), Regime 2 (competitive integration), and Regime 3 (task-appropriate selection)<n>We validate the framework across Claude Sonnet 4.6, GPT-5.5, Gemini 2.5 Flash, Llama 4 Maverick, and DeepSeek V3 using 9,970 API calls in three experimental phases.
arXiv Detail & Related papers (2026-05-12T06:00:48Z) - Trivial Vocabulary Bans Improve LLM Reasoning More Than Deep Linguistic Constraints [0.0]
A previous study reported that E-Prime selectively altered reasoning in language models.<n>I designed a replication with active controls to test the proposed mechanism.<n>I present these findings as a case study in discovery through disconfirmation.
arXiv Detail & Related papers (2026-04-03T03:48:27Z) - How Does Prefix Matter in Reasoning Model Tuning? [57.69882799751655]
We fine-tune three R1 series models across three core model capabilities: reasoning (mathematics), coding, safety, and factuality.<n>Results show that prefix-conditioned SFT improves both safety and reasoning performance, yielding up to +6% higher Safe@1 accuracy.
arXiv Detail & Related papers (2026-01-04T18:04:23Z) - Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation [9.099589602551573]
We study universal adversarial suffixes that, when appended to any input, broadly reduce accuracy across tasks and models.<n>Our approach learns the suffix in a differentiable "soft" form using Gumbel-Softmax relaxation and then discretizes it for inference.<n>A single suffix trained on one model transfers effectively to others, consistently lowering both accuracy and calibrated confidence.
arXiv Detail & Related papers (2025-12-09T00:03:39Z) - Causal Understanding by LLMs: The Role of Uncertainty [43.87879175532034]
Recent papers show LLMs achieve near-random accuracy in causal relation classification.<n>We investigate whether pretraining exposure to causal examples improves causal understanding.
arXiv Detail & Related papers (2025-09-24T13:06:35Z) - Small Edits, Big Consequences: Telling Good from Bad Robustness in Large Language Models [0.0]
Large language models (LLMs) now write code in settings where misreading a single word can break safety or cost money, yet we still expect them to overlook stray typos.<n>To probe where useful robustness ends and harmful insensitivity begins, we compile 50 LeetCode problems and craft three minimal prompt perturbations.<n>Six frontier models, including three "reasoning-tuned" versions, solve each mutated prompt.
arXiv Detail & Related papers (2025-07-15T03:22:07Z) - Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions [103.20281438405111]
Multiple-choice question answering (MCQA) is a key competence of performant transformer language models.<n>We employ vocabulary projection and activation patching methods to localize key hidden states that encode relevant information for predicting the correct answer.<n>We show that subsequent layers increase the probability of the predicted answer symbol in vocabulary space, and that this probability increase is associated with a sparse set of attention heads with unique roles.
arXiv Detail & Related papers (2024-07-21T00:10:23Z) - How Can We Know When Language Models Know? On the Calibration of
Language Models for Question Answering [80.82194311274694]
We examine the question "how can we know when language models know, with confidence, the answer to a particular query?"
We examine three strong generative models -- T5, BART, and GPT-2 -- and study whether their probabilities on QA tasks are well calibrated.
We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness.
arXiv Detail & Related papers (2020-12-02T03:53:13Z) - How do Decisions Emerge across Layers in Neural Models? Interpretation
with Differentiable Masking [70.92463223410225]
DiffMask learns to mask-out subsets of the input while maintaining differentiability.
Decision to include or disregard an input token is made with a simple model based on intermediate hidden layers.
This lets us not only plot attribution heatmaps but also analyze how decisions are formed across network layers.
arXiv Detail & Related papers (2020-04-30T17:36:14Z)
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.