Capability evidence: sampling, readout, and reliability¶
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Foundation models / 56 questions · 14 papers
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56 questions · 14 papers
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why don't successful compilation and crash-free execution establish XLA correctness?
XLA from the Trenches: Correctness on TPU Workloads at Scale
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Answer: Silent miscompilations can return finite, correctly shaped tensors while indexing, rounding, buffers, or synchronization are wrong. Completion and briefly normal loss miss failures with no error signal. The paper uses differential references on valid inputs plus hierarchical isolation, not launch success as correctness evidence.
Illustrative example: Teaching example: A narrow-integer iota wrap can make a write at index 5 also modify 261. Execution completes; comparison against correct indexing semantics exposes it.
Sections in the Chinese source: 背景与主要改动, 核心机制与数值例子. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why combine backward input constraints with VREG inspection inside intact fusions?
XLA from the Trenches: Correctness on TPU Workloads at Scale
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Answer: Backward constraints prevent invalid inputs such as negative square roots or division by zero from creating false differences. Macro isolation only finds a fusion; lowering faults inside it still require register intermediates. Splitting it changes memory precision and pipelining and can hide the bug, so a correct split graph does not validate the original fusion.
Illustrative example: Teaching example: r=x−f32(f16(x)) becomes zero if VREG rounding is skipped. Splitting for debugging can spill to 16-bit HBM and restore rounding. Inspect the original fusion rather than replacing it.
Sections in the Chinese source: 核心机制与数值例子, 资源与复现边界. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Why are 142 resolved bugs, 103 under review, and online A/B gains three different kinds of evidence?
XLA from the Trenches: Correctness on TPU Workloads at Scale
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Answer: Resolved counts describe defects closed through engineering review. Pending findings can still be rejected or revised, so they do not make 245 resolved bugs. User A/B requires actual randomized comparisons and business metrics, not inference from bug counts. The paper qualifies as Google systems infrastructure, not as industrial recommendation evidence passing an A/B gate.
Illustrative example: Teaching example: Of 10 submitted patches, if 6 landed and 4 are pending, 6 are completed, not 10. Even landing all 10 supplies no randomized CTR experiment.
Sections in the Chinese source: 论文工程与线上证据, 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
With OpenXLA public, why mark the full paper not reproduced instead of running an A100 substitute?
XLA from the Trenches: Correctness on TPU Workloads at Scale
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Answer: Public macro isolation and evaluation are only part of the pipeline. Production graphs, the internal agent harness, target ISA/MXU simulation, VREG hardware stepping, and physical-TPU confirmation do not come with the framework. A100 has different register, rounding, and compilation semantics; a CUDA example cannot validate TPU defects. Resource and availability mismatch justifies review DONE/not reproduced rather than fabricated success.
Illustrative example: Teaching example: Correctly executing two casts in CUDA does not show that TPU lowering retained them. Matching outputs do not cover the original TPU-generated code.
Sections in the Chinese source: 已核查与未实现, 资源与复现边界. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why scan contrastive input activations?
Detecting Safety Training Modification in Language Models via Activation Analysis
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Answer: Identify safety-related separation in intermediate layers and compare direction/drift with a matched baseline. This does not generate or validate behavioral answers.
Illustrative example: Separable concepts do not guarantee all related requests are handled well.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why doesn't AMS activation separation establish behavioral safety?
Detecting Safety Training Modification in Language Models via Activation Analysis
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Answer: Mid-layer contrast directions and same-architecture baseline drift provide structural evidence only. Layer numbering, variance, and baseline affect thresholds. Independent behavior evaluation is still required.
Illustrative example: A model can distinguish safety concepts internally while still responding unsafely to a particular prompt.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
What do AMS Tier 1 and Tier 2 measure?
Detecting Safety Training Modification in Language Models via Activation Analysis
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Answer: Tier 1 measures separation of positive and negative projections. Tier 2 compares direction consistency and relative drift against matching layers/dimensions. Internal separation need not mean baseline alignment.
Illustrative example: Wide separation can coexist with a direction flip relative to baseline.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why add one to the AMS block index in Hugging Face hidden_states?
Detecting Safety Training Modification in Language Models via Activation Analysis
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Answer: Block hooks are zero-based but hidden_states[0] is embeddings. Use block_id+1, select the last real token, and preserve population variance and strict threshold.
Illustrative example: Block 9 reads hidden_states[10].
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why bias vocabulary partitions by a periodic pattern?
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
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Answer: Tag-specific logit biases induce a statistical periodic structure. The detector compares windows across all cyclic shifts for watermark and edit evidence.
Illustrative example: AB partition alternation is detectable without knowing where the observed text starts.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why can testing all cyclic phases still miss full-period deletion?
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
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Answer: Phase search handles global offsets, but deleting a whole period can preserve the repeating pattern and phase. Independently calibrate global/local thresholds; a hard-pattern diagnostic is not perfect soft-watermark localization. No full local model edit matrix.
Illustrative example: Removing one full red/blue cycle leaves the remaining pattern unchanged.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Can global D and local E(t) thresholds replace each other?
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
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Answer: D detects a watermark across the whole passage; E(t) uses window coverage to flag possible edits. They differ in scope and threshold direction. Calibrate false positives independently.
Illustrative example: Global passage can pass while a short span scores low; that does not precisely identify every edit.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Can local E(t) zero-pad edges?
Detecting Post-generation Edits to Watermarked LLM Outputs via Combinatorial Watermarking
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Answer: No. It is defined only for complete interior t=w−1 through T−w, without zero padding or changing denominators. Check all phases and preserve full-period deletion as a blind negative control.
Illustrative example: Edge positions with incomplete windows should not be falsely flagged because zeros were added.
Sections in the Chinese source: 核心公式, 本地复现. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why build CritBank from weak-model errors?
CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
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Answer: Cluster recurring failures offline into reusable reminders for stronger models. This moves reflection cost upstream and avoids requiring a first failure on each deployed request.
Illustrative example: Add a common missed-unit-conversion reminder before a new answer.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why doesn't CritICL need the strong model to fail first at deployment?
CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
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Answer: Offline weak-model failures become reusable critiques. Static mode uses common patterns and dynamic mode retrieves question-relevant ones before one strong-model generation. Transfer patterns, not weak answers; relevance and coverage matter.
Illustrative example: Retrieve a unit-conversion reminder before answering, without reading test gold to craft it.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
How do static and dynamic critique selection differ?
CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
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Answer: Static uses frequent global failures; dynamic retrieves relevant CritBank entries for the current input. Both act before generation, but only dynamic adds input-dependent retrieval.
Illustrative example: A math question can retrieve calculation reminders different from those for other requests.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why not select critiques with the current question's correct answer?
CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
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Answer: That leaks evaluation targets into prompts. Dynamic retrieval uses only input similarity to the existing bank and then generates once. Offline failures and current hidden gold stay separate.
Illustrative example: Looking up the solution before picking the critique that fixes it is not deployable retrieval.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why explore multiple languages for one knowledge question?
Cross-Lingual Exploration for Parametric Knowledge
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Answer: Knowledge may be more accessible under some language contexts. Explore across languages then aggregate semantic answers, not mere string translations or presumed-correct majority.
Illustrative example: Distinct expressions may refer to one entity.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why not replace cross-lingual semantic aggregation with string voting, and why count aggregation cost?
Cross-Lingual Exploration for Parametric Knowledge
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Answer: Different languages may phrase equivalent facts differently, and superficially similar errors can differ. The method uses an LM for majority/minority-aware aggregation and separate correctness/consistency judgment. All path and aggregation tokens count. This is literature-only locally.
Illustrative example: Two names for one city need entity matching, not separate votes or costs counted from only one path.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Why can't a string counter substitute for semantic model aggregation?
Cross-Lingual Exploration for Parametric Knowledge
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Answer: Equivalent meanings can use different strings and equal strings can differ in context. An LM performs majority/minority-aware aggregation and semantic judgments in the paper.
Illustrative example: “United States” and “USA” can be one answer, despite string counts splitting them.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why count more than generated path tokens?
Cross-Lingual Exploration for Parametric Knowledge
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Answer: Include each path's inputs and aggregation model tokens; semantic correctness and consistency also use model calls. Local work did not run the APIs or provide heuristics as substitutes.
Illustrative example: Ten short generations can still produce a long, expensive aggregation prompt.
Sections in the Chinese source: 核心公式, 本地复现. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why not report only one DeepSEA multilingual average?
DeepSEA: A Multilingual Evaluation Benchmark for Southeast Asia
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Answer: Language-resource strata and tasks expose gaps hidden by a mean. The project provides data and evaluation protocol, not a parameter-training algorithm.
Illustrative example: High-resource successes can mask low-resource failures.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why fix prediction order and isolate gold in grouped DeepSEA reporting?
DeepSEA: A Multilingual Evaluation Benchmark for Southeast Asia
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Answer: Validate language, size, and order so predictions align to their examples. Expose only public task fields; keep answers and annotations with the evaluator. This evaluates lower-resource languages, not model training.
Illustrative example: Swapped outputs across languages become alignment errors; the predictor must not see references.
Sections in the Chinese source: 背景与主要改动, 核心公式与评分口径. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Are deterministic scorers and LLM judges interchangeable in DeepSEA?
DeepSEA: A Multilingual Evaluation Benchmark for Southeast Asia
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Answer: Follow the fixed upstream protocol for each task. Substituting judges changes comparability. Bind outputs to language, subject, and row; group summaries do not fix misalignment.
Illustrative example: Pairing language A row one with a language B answer invalidates scoring even if row counts match.
Sections in the Chinese source: 背景与主要改动, 公共输入与 Gold 隔离. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Should prediction callbacks get the evaluator or benchmark file and promise not to read gold?
DeepSEA: A Multilingual Evaluation Benchmark for Southeast Asia
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Answer: No. Use allowlisted immutable public input and separate deep-copied private references. Reject missing languages, bad counts, or nonstring outputs. This is data-flow isolation, not an OS sandbox; do not add shell bypasses.
Illustrative example: Provide question, options, and context, not raw answer_with_cot records.
Sections in the Chinese source: 公共输入与 Gold 隔离. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why make detection depend on inspectable sparse features?
Building Auditable AI Text Detectors with Sparse Autoencoders
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Answer: Pool SAE features, filter unreliable concept descriptions, and fit a sparse linear head so logits decompose into actual computed contributions rather than post-hoc reasons.
Illustrative example: Verify a contribution by zeroing its feature rather than trusting generated explanations.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why is zeroing a DetectorScope concept exact in logit space but not equivalent to editing text?
Building Auditable AI Text Detectors with Sparse Autoencoders
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Answer: Sparse pooled features feed a linear head, so each weight times activation is an exact contribution. Text edits can change several features and contexts. Automatic descriptions are imperfect; local head excludes the full SAE/explanation pipeline.
Illustrative example: Zero one feature to verify its score change; deleting a word can alter several concepts.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Is description validation equivalent to human confirmation of concept semantics?
Building Auditable AI Text Detectors with Sparse Autoencoders
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Answer: Spearman correlation compares observed and predicted activation ranks as a filter, not manual validation or safety. Constant sequences do not support the correlation.
Illustrative example: Correlation on a small sample does not prove meanings are correct for every text.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why not apply an L1 subgradient and soft thresholding together?
Building Auditable AI Text Detectors with Sparse Autoencoders
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Answer: Apply BCE SGD with momentum, then ηλ soft thresholding, without a second L1 gradient or bias penalty. Fit uncentered RMS on training only.
Illustrative example: Penalizing twice changes effective sparsity beyond λ.
Sections in the Chinese source: 核心公式, 数据协议与结果. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why not average verifier scores in FUSE?
FUSE: Ensembling Verifiers with Zero Labeled Data
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Answer: Correlated errors get counted repeatedly. Estimate query-conditioned quality from scores, transform to mitigate dependencies, then build posteriors for ensemble optimization.
Illustrative example: Three near-identical verifiers need not add three times the evidence.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why doesn't label-free FUSE get more reliable simply by adding correlated verifiers?
FUSE: Ensembling Verifiers with Zero Labeled Data
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Answer: Moment estimation needs identifiability assumptions such as a better-than-random majority and conditionally independent triplets. Transforms mitigate correlation, but shared errors can remain. Posteriors are not gold, which stays evaluation-only.
Illustrative example: Three verifiers copying one wrong rule are not three independent correctness signals.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Do moment-based posteriors provide the same guarantees as gold labels?
FUSE: Ensembling Verifiers with Zero Labeled Data
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Answer: No. They depend on identifiability; high correlation or constant scores can break estimation. High estimated probability is not truth or safety.
Illustrative example: Shared wrong rules can produce agreement on the wrong answer.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Can gold choose score transforms or ensemble parameters?
FUSE: Ensembling Verifiers with Zero Labeled Data
Show answer and example
Answer: Not in the documented label-free fit. Use score matrices only and reserve labels for independent evaluation. Choosing transforms by gold on the same questions breaks both boundaries.
Illustrative example: Trying transforms and selecting the best by answers is not unsupervised selection.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why use a multiway crossing for two-document conflicts?
Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4
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Answer: Word identity, source, and position can all influence choice. Four conditions separate them instead of mistaking lexical or ordering preference for trust.
Illustrative example: Preferring word B is different from preferring whichever word the new source supplies.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why swap both word identity and document order in conflict tests?
Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4
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Answer: Order alone confounds lexical preference, source role, and position. Four orthogonal crossings estimate effects, with a shared filtered set and unfiltered sensitivity analysis. Conclusions are for one model on short English choices.
Illustrative example: A favored word can win in either position; swap which source provides it to isolate source and order.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Why distinguish order null contrast O from positional effect S?
Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4
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Answer: They use different sign combinations. S measures position and negative S favors first; O is a null control. Both are half effects, so double for full range.
Illustrative example: Changing the signs on e1–e4 yields a different contrast.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Can failed or degenerate BCa intervals silently fall back to equivalence?
Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4
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Answer: No. Keep failed/degenerate and equivalent=null, not a hidden percentile interval. Ten conditions share the union of exclusions, and unfiltered sensitivity remains.
Illustrative example: If bootstrap cannot form a valid interval, report undecided rather than a pass.
Sections in the Chinese source: 核心公式, 实现与数据协议. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why compare instruction null with bare null in attention reranking?
How Calibration Content Shapes Attention-Based Reranking
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Answer: Instructions can affect scores even without a query. Interpolation separates that influence and controls which baseline is subtracted from query-plus-instruction scores.
Illustrative example: “Judge relevance” may raise document scores without conveying the user's query.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why is λ=0 still calibrated in interpolated null calibration?
How Calibration Content Shapes Attention-Based Reranking
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Answer: Zero removes the instruction-null contribution but still subtracts bare-null. Align all three score vectors by document and select λ on validation. A global oracle weight is not held-out gain. Local work is a score kernel.
Illustrative example: A long document can score highly without a query; bare-null still removes this baseline.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
What do standard ICR, bare-null, and no calibration subtract?
How Calibration Content Shapes Attention-Based Reranking
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Answer: λ=1 subtracts instruction null; λ=0 subtracts bare null; only uncalibrated subtracts neither. Document all three scoring inputs and subtraction terms.
Illustrative example: A nonzero s(empty,empty) can make λ=0 rank differently from raw s(q,I).
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Can equal-length score arrays be subtracted directly?
How Calibration Content Shapes Attention-Based Reranking
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Answer: Document IDs and ordering must match, not just shape. Choose λ on validation, not a full-data oracle. The local score operator is not a real attention extractor.
Illustrative example: If pass two swaps docs A and B, positional subtraction applies the wrong null score.
Sections in the Chinese source: 背景与主要改动, 本地复现. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why observe SAE features instead of words in Mechanistic ETM?
Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
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Answer: Sparse activations express model-internal concepts. A frozen SAE encoder maps topics to feature probabilities and fits document proportions to counts.
Illustrative example: Different words may activate similar concepts, and one word need not indicate the same topic in every context.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why use an SAE encoder for topic probabilities but a separate decoder for steering?
Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
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Answer: The encoder maps topic directions to feature activation probabilities for a binomial count likelihood. The decoder maps feature combinations into residual space for intervention. Do not tie them implicitly. Steering removes the old parallel component before adding desired strength. Local work omits large-model experiments.
Illustrative example: Estimate sparse feature occurrence, then decode an intervention direction; an equally shaped encoder weight is not a substitute.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Why model feature counts with binomial rather than ordinary multinomial bags?
Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
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Answer: Each feature can occur once per token but features are not mutually exclusive. Model each count with N_tokens trials instead of forcing total counts to token count.
Illustrative example: One token activating two features can make total feature count exceed number of tokens.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Does ETM steering strength zero leave activations unchanged?
Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders
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Answer: Not necessarily. Remove the existing parallel component, add λs, and restore decoder bias. λ=0 still removes the component. Use an independent decoder, not encoder-tied weights.
Illustrative example: A strong original topic projection is removed at zero strength rather than preserved.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why reuse historical per-item scores?
Show answer and example
Answer: Cross-model correlations form a probabilistic surrogate, updated after limited queries to estimate total score and uncertainty. Estimates are not full evaluations.
Illustrative example: Query several items and predict the rest while reporting posterior variance.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why select a query before reading target scores and exclude the target from history?
Show answer and example
Answer: Historical correlations supply a prior. Only actively selected responses update the posterior. An entire target column in covariance or selection leaks unqueried answers. Variance reduction does not guarantee robustness beyond historical distribution.
Illustrative example: Select the most informative item first and query its score, rather than preload the target's full column.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
How does Bayesian quadrature acquisition differ from choosing the lowest current mean?
Show answer and example
Answer: BQ maximizes expected reduction of overall wᵀKw variance, not difficulty. Failure-oriented acquisition has separate candidate conditions.
Illustrative example: An easy item correlated with many unqueried items can reduce score uncertainty substantially.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why reject duplicate target observations and target IDs in history?
Show answer and example
Answer: Duplicates distort query budget and posterior updates; historical target scores leak unqueried outcomes into the prior. Only selected feedback is allowed. Three history records are a local minimum, not a theorem guarantee.
Illustrative example: A target column already present in history invalidates claimed savings.
Sections in the Chinese source: 核心公式, 代码映射与边界. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why share real feedback across adaptive policies?
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
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Answer: Separate A/B/n runs may pay repeatedly for identical context/action pairs. Coupling permits valid reuse while preserving each policy's marginal trajectory distribution.
Illustrative example: If two policies choose the same action in the same state, one feedback can update both.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why match full context and action before sharing TCAB feedback?
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
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Answer: Identical actions can yield different outcomes under different contexts. Tree maximal coupling matches complete pairs to share conditional rewards while preserving each policy's marginal trajectory. Coupled policies are deliberately correlated, and savings do not prove global optimality.
Illustrative example: Picking the same product for different users does not justify sharing one click observation.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Does correlated coupling alter each policy's own trajectory distribution?
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
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Answer: Not necessarily. It changes joint dependence while preserving individual marginals. Equal final accuracy alone does not verify adaptive updates or marginal trajectory parity.
Illustrative example: Two draws can coincide more frequently while each retains its original probabilities.
Sections in the Chinese source: 背景与主要改动. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why not count identical action strings as saved feedback calls?
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
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Answer: Reuse depends on full context and state conditions. The same action in different environments can yield different feedback. Compare actual shared queries and tree-edge differences.
Illustrative example: Two different users choosing A do not yield the same reusable observation.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Basics
Why use tasks with deterministic algorithmic answers?
XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
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Answer: Controlled difficulty and exact answers reduce open-ended judging ambiguity. Compare real models across validated language versions; a scorer alone is not capability evidence.
Illustrative example: A state transition can use the same exact solver after changing its language.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Core mechanism
Why don't correct local algorithm answers establish cross-lingual gaps?
XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
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Answer: Exact scoring removes judge subjectivity, but validated translations and real model outputs across complexities are needed. Local generation has limited structure, not the full noise/difficulty matrix. Gold stays in the scorer, and five task diagnostics do not measure language accuracy.
Illustrative example: An internal shortest-path solver can answer a graph, without testing whether two natural-language versions are equivalent or equally solvable.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Method comparison
Is scorer correctness evidence of multilingual generalization?
XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
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Answer: No. It validates evaluation of a supplied prediction. Generalization needs real inputs and outputs across languages, complexity scans, and statistics. Small examples do not substitute for seven languages or human translation checks.
Illustrative example: Solving a 12-node shortest path internally does not show an LM can answer it in a specific language.
Sections in the Chinese source: 本地复现, 复现边界. The example illustrates the mechanism.
Foundation models / Capability evidence: sampling, readout, and reliability
Implementation details
Why does Pr-DSA top-eight scoring ignore order but reject duplicates?
XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
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Answer: The target is the complete eight-state set after full probability propagation. Reordering is harmless; repeats hide missing states. Other tasks also use full JSON matching, not partial credit.
Illustrative example: Eight list entries beginning A,A,B may contain fewer than eight distinct states and must be rejected.
Sections in the Chinese source: 核心公式. The example illustrates the mechanism.
Topic guide and reading order
Start with this question¶
How can we tell whether a high score reflects the intended capability rather than language, ordering, readout, or statistical bias?
Takeaway¶
XLGoBench holds the underlying task constant with comparable multilingual algorithmic problems and objective answers, then varies language and complexity. ProEval fits a probabilistic surrogate from past item scores and uses uncertainty to select informative inputs, saving expensive ratings. Activation probes, reranking calibration, and watermarks audit other observation channels. Define the target capability and observable first.
Example¶
Translate a shortest-path problem while keeping the graph and correct path fixed. If quality drops, check that the translation preserved constraints. A 100-item budget can use active selection, but then the selected set is not representative. Use the prescribed estimator and uncertainty, not the raw average on difficult cases.
Common pitfall¶
Detection scores, operator contract pass rates, and model capability are different. Surrogates may use history but not unseen target scores or gold. One cross-language difference is not a full language-capability claim. Local ProEval and XLGoBench scorer diagnostics do not reproduce the full papers.
Reading order¶
Start with XLGoBench on controlled task, language, and difficulty. Then read ProEval on GP surrogates, active selection, and population estimation. Follow with probing, calibration, safety detection, or online experiments.
- XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks · Original paper
- ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation · Original paper
Check your understanding¶
If actively selected likely failures have low mean accuracy, can we directly estimate overall online accuracy?
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No. Selection by failure likelihood introduces bias. Define the target distribution and use a matching estimator or representative sample. Report failure discovery and overall estimation separately.