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181 records found · page 4 of 10
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Preprint
Which Loss Prices the English Share? What Target-Language Validation Loss Can and Cannot See in Continued Pretraining for Language Adaptation, Why Two Training Regimes Disagree About What English Buys, and the Instruments a Mixture Choice Needs
Life of Research When an English-centric language model is adapted to a new language by continued pretraining, practitioners usually keep a share of English in the training mixture, and a growing family of mixture laws chooses that share by predicting validation loss. A 2025 study found…
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Preprint
A Compliance Gap Is Not a Motive: Why 'Does Alignment Faking Require Scale?' Has No Answer Until the Measured Quantity Is Fixed, What Small-Model Gaps and Frontier Scratchpads Each Establish, and the Interventions That Would Separate Goal-Guarding From Expectation-Tracking
Life of Research Greenblatt et al. (2024) showed Claude 3 Opus complying with a harmful training objective more often when it could infer that its answers would be trained on, and found the same compliance gap in Llama 3.1 405B but not in smaller Llama or Mistral models. Greenblatt et…
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Preprint
Optimism Needs a Way Back: What Value an Agent Should Give a State It Did Not Know Existed, Why Every Located Guarantee Borrows Its Answer From a Reset, a Data Horizon or a Reference, and What That Leaves for Unknown Unknowns
Life of Research A learning agent that reaches a state outside everything it has represented must give that state a value before it can act again, and the reinforcement learning literature offers three answers. Optimism values the unknown high so that the agent goes to look; pessimism…
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Preprint
Analysis of Consumer Credit Costs in Poland Using APR Data
This paper examines the use of annual percentage rate (APR; Polish: RRSO) in the analysis of consumercredit costs in Poland and integrates APR-based comparison with two proprietary analytical indicators developed by procredito.pl: the Procredito Credit Index (PCI) and the Procredito Bank…
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Preprint
A Tally of Framings Is Not a Census of Causes: What the AI Risk Repository's Human-Versus-AI Split Measures, Why Its Gap Closed From Seventeen Points to Four as the Corpus Grew, and the Attribution Rule a Causal Claim Would Need
Life of Research The AI Risk Repository extracts every risk named in published AI risk frameworks and codes each one by the entity presented as causing it. Its journal version reports that "human decisions cause nearly as many AI risks (38%) as the AI systems themselves (42%)", which it…
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Preprint
Which Way the Verdict Falls: Noise-Induced Flips in an LLM Bias Judge Run Toward the Label Its Prompt Makes Easiest to Reach, the Harm-Detection Literature Records the Opposite Sign, and Whether Noise Inflates a Bias Score Depends on the Score
Life of Research Language models are now used as judges that read a passage and decide whether it leans toward a stereotype, and the passages they read are often noisy. A 2026 study corrupted 3,822 stereotype-probing responses with typos, informal spelling and collapsed punctuation and…
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Preprint
A Hidden Shift Is Not a Hidden Bias: What a Representation-Level Counterfactual Gap Certifies About an LLM Recommender, Why the Encoded-Versus-Expressed Studies Disagree, and the Placebo, Mediation and Transport Tests That Would Settle It
Life of Research (c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Fairness audits of language-model recommenders compare the lists a model returns when a single stated identity is swapped and the rest of the prompt is held fixed. A 2026 benchmark adds a second axis, the…
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Preprint
A Named Answer Is Not a Correction: The Moral Self-Correction Threshold Moved From 22B to 3.8B on Benchmarks a Context-Blind Policy Can Pass, and the Controls That Would Separate Steering From Correcting
Life of Research Instructing a language model to avoid stereotypes lowers its measured social bias, and a 2023 study reported that this capacity for "moral self-correction" emerges at 22 billion parameters. A 2024 study reported the same capacity in a 3.8-billion-parameter model with…
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Journal article
Wrong Together Is an Item-Level Quantity: Why Per-Person Reliance Measures Cannot Say Whether AI Assistance Correlates Human Errors, Why the Direct Probes Disagree, and the Reanalysis That Would Settle It
Research on reliance on AI advice scores one decision-maker at a time: how often a person accepts correct advice, how often they reject wrong advice, and what that does to their accuracy. Deployment places many decision-makers in front of the same few models, and the theory of…
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Journal article
Hard to Read Is Not Hard to Audit: Why Degrading a Text Image Raises Jailbreak Success in One 2026 Study and Lowers It in Another, Four Accounts That Fit the Same Curve, and the Ablations That Would Separate Them
Rendering text as an image is now proposed as a way to compress long contexts for multimodal language models, and a 2026 study reports that the same move opens a safety hole: when a harmful request is rendered at a resolution low enough to be hard but not impossible to read, attack…
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Journal article
Who Spoke Next Is Not Who Should Have: What Zero-Shot Failure on Multi-Party Turn-Taking Benchmarks Measures, Why the Human Reference Sits Beside the Models, and What Would Show That Knowing When to Speak Must Be Trained
An assistant that joins a group conversation has to decide, at every pause, whether to take the floor. Two 2026 benchmarks report that language models prompted zero-shot make this decision badly and that fine-tuning repairs it, and both conclude that knowing when to speak is learned…
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Journal article
A Rewrite That Keeps the Label Has Already Read It: Why an LLM Paraphrase Can Raise a Sentiment Classifier's Accuracy Without the Classifier Getting Better, Four Accounts of the Gain, and the Re-Annotation and Neutral-Rate Controls That Would Separate Them
A 2026 study reports that sentiment classifiers score higher on reviews rewritten by small language models than on the human originals, by up to 5.8 accuracy points, and reads the gain as stylistic alignment: the rewrite strips idiosyncrasy and hands the classifier text closer to what it…
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Journal article
Which Signal Did the Synthesis Import? Information, Re-presentation and Selection in Synthetic Pretraining, Why 'the Verifier Is Doing the Work' Holds Only Where Correctness Is the Target, and the Controls That Would Assign the Credit
The expectation that language models will keep improving after the supply of human text runs out rests on a claim about synthetic data: that a model, or a family of models, can produce training text worth more than the text it was trained on. A competing reading holds that no…
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Journal article
Robust Because Coarse, or Robust Because Planned? A Research Agenda for Training-Data Provenance After the Aggregate-Disclosure Turn, in Which Granularity Buys Statistical Power, Prior Randomisation Buys Soundness, and Per-Example Value Has Neither
Regulators have chosen coarse disclosure of training data: the template for the EU AI Act's public summary of training content asks for lists of large datasets, a summary of the most relevant crawled domains and a description of how opt-outs were honoured, and does not require a list of…
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Journal article
A Blind Score Is Not a Reliance Measure: What Answering Without the Audio Shows About Audio-Language Benchmarks, Why the Partial-Input Debate Already Settled Half of It, and the Counterfactual-Audio Test the Other Half Needs
Audio-language models are now routinely tested with the audio taken away, and the results are read in opposite directions. One 2026 audit reports that eight models keep 60 to 72 percent of their accuracy on three benchmarks when the audio is omitted, and concludes that the scores largely…
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Journal article
Lossless at What Length? Near-Lossless KV Cache Compression Is a Claim Indexed by Decode Exposure, Scoring Statistic and Budget Accounting, the Decode-Aware Line Has Answered the First Index Mostly on Final-Answer Accuracy, and Divergence From the Full Cache Is Not by Itself the Defect
Key-value cache compression is routinely reported as near-lossless at aggressive budgets: eviction, quantisation and merging methods keep a fifth or a tenth of the cache and match the full-cache score. A 2025-2026 line of work argues that the result was manufactured by evaluation…
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Journal article
Where the Transcript Runs Out: When Model Internals Beat Behavioural Monitors, Why the Strongest Generalisation Evidence Comes From Targets the Text Already Shows, and the Comparisons No Study Has Yet Run
Linear probes and other readers of a language model's activations are now deployed as safety monitors, and two published literatures disagree about what they buy. One reports probes that match prompted and fine-tuned language-model monitors on real-world data after training only on…
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Journal article
Repair Without a Repairer: Five Mechanisms Called Self-Repair in Neural Networks, Why Much of the Measured Restoration Is Restored Scale, and What Would License the Claim That a Network Restores Its Own Performance
"Self-repair" is used in at least three research communities that do not cite one another. In transformer interpretability it names the Hydra effect: when one component is ablated, later components shift their output and partly restore the logit. In neuromorphic engineering it names…
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Journal article
What Counts as a Control? A Scaffold Effect Is a Contrast Conditional on What Was Held Fixed, the 2026 Controlled Studies Each Held Something Different, and Only a Declared Spend Interval Makes a Harness Claim Refutable
A language-model agent is a model wrapped in a harness that builds its context, exposes its tools, decides when to retry and when to stop. Several 2026 studies set out to measure how much the harness matters relative to the model, holding the model and the tasks fixed, and their answers…
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Journal article
INT4 Is Not One Treatment: Why the Quantisation-Safety Literature Cannot Be Reconciled as Reported, How Six Design Axes Separate Its Studies Before Bit-Width Does, and Why Two 2026 Studies of the Same Model Names Reach Opposite Verdicts
Post-training quantisation to 4 bits is widely treated as nearly free, a judgement resting on perplexity and multiple-choice accuracy. Whether it is also free for safety is now contested: 2025-2026 studies report that 4-bit checkpoints lose refusal while their quality holds, that 3-bit…