Typed Readout: From Generated Sentences to Candidate Distributions¶
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Structured Decisions / 36 questions · 9 papers
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36 questions · 9 papers
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
What output tasks suit Chinese-Jev?
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
Show answer and example
Answer: Finite-choice decisions such as classification, routing and candidate selection. It takes Chinese context and candidates and directly returns candidate probabilities rather than generating long free-form answers.
Illustrative example: Example: Route an inquiry to shipping or refunds from a supplied list; writing an open-ended article is a different interface.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why score Chinese-Jev candidate markers rather than generate option names and parse them?
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
Show answer and example
Answer: Closed decisions need a distribution across candidates. Encode context and candidates, score each marker's hidden state and normalize, avoiding free generation and parsing. Soft targets retain annotation probabilities. A candidate's share depends on its set and is not independent correctness probability; the local objective does not reproduce general pretraining.
Illustrative example: Example: A ticket receives refund, shipping and account probabilities. Adding a category changes the shares; the old refund probability isn't fixed confidence.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
Do one-hot and soft labels give Chinese-Jev the same supervision?
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
Show answer and example
Answer: No. One-hot keeps a single target; soft distributions preserve probability allocation across choices, reflecting ambiguity or annotator differences. The readout is shared, not the supervision. Soft labels need trustworthy provenance and are not true accuracy by virtue of precision.
Illustrative example: Example: Labels 0.6 and 0.4 retain a close preference; 1 and 0 lose it.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Which axis should Chinese-Jev softmax use?
Chinese-Jev: Bringing System One Model to Chinese-Language Tasks
Show answer and example
Answer: Normalize over valid candidates within one request. Score marker states with the same function. Do not share a denominator across requests; align soft-label order to candidates.
Illustrative example: Example: Two tickets with three categories each require two distributions, not one over six scores.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
Does EditJev predict the edited image?
EditJev: System One Routing for Image Editing Models
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Answer: No. It predicts an expected-utility vector for fixed editors and routes the request to one. That editor produces the image; router supervision comes from judging offline outputs across editors.
Illustrative example: Example: For a clothing recolor, the router picks an editor rather than painting the result.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why run every editor offline but only the selected one at inference?
EditJev: System One Routing for Image Editing Models
Show answer and example
Answer: All offline outputs and their judgments supply pairwise quality labels. At deployment, predicted utility plus fixed speed preference selects once without seeing outputs. Candidate ordering must match; speed weighting is a tradeoff, not truth. The full multi-editor setup is too costly and was not reproduced.
Illustrative example: Example: For background removal, pick an editor first and call only it. Running every editor defeats the cost savings.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How can EditJev express quality-first and speed-first choices in one interface?
EditJev: System One Routing for Image Editing Models
Show answer and example
Answer: Combine quality utility with a fixed speed score weighted by beta. Changing beta changes the choice, not the quality labels. It expresses a cost preference, not a universally best setting without evidence.
Illustrative example: Example: If quality is close, weighting speed more can select the faster editor without making it higher quality.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Why normalize EditJev's pairwise loss by valid pairs per request first?
EditJev: System One Routing for Image Editing Models
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Answer: Requests have different counts of reliable pairs. Average within request and then across requests to prevent pair-rich requests dominating. A request with no valid pair contributes differentiable zero.
Illustrative example: Example: An image with many easily distinguished outputs shouldn't outweigh all others simply because more pairs can be formed.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
Does a new GLiClass runtime label create a new classifier output neuron?
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
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Answer: No. Labels are input text scored for the current request. Candidate descriptions/set change, not a fixed output parameter per label. Semantics must be carried by content and still require validation; allowing new labels is not arbitrary domain expertise.
Illustrative example: Example: Replace the refund inquiry description with shipping follow-up, rather than reusing a class number with permanent meaning.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why can't runtime GLiClass reduce to a fixed text-only classifier?
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
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Answer: Scoring must use candidate content to handle changing label sets; a fixed index cannot convey new descriptions. Joint candidate scoring avoids per-candidate calls and text parsing. Local bilinear/centered scoring checks the mechanism without full weights or training.
Illustrative example: Example: The same inquiry may require different choices in account and credit-card taxonomies. New labels need descriptions, not unseen numbers.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does GLiClass dynamic scoring differ from pairwise cross-encoders?
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
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Answer: Pairwise methods infer separately for each text-label pair. This interface processes candidates together and emits a joint distribution. Calls and normalization differ. The local lightweight interaction kernel is not a full Transformer or a speed benchmark.
Illustrative example: Example: Ten labels do not necessarily require ten independent LM calls; actual throughput depends on implementation.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
What does local rival-centered GLiClass center?
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Show answer and example
Answer: Representations of candidates within the same question, before scoring their interaction with state. Other requests do not enter the mean. This tests relative candidate interaction, not original weights or PPO.
Illustrative example: Example: Refund and shipping candidates share their ticket's center, not labels from another ticket.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
Are relevance annotation and reranking the same output in Portuguese JEV retrieval?
Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking
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Answer: No. Annotation assigns ordinal relevance; reranking uses independent probabilities that each document directly answers a query. Metrics and semantics differ; class shares are not independent relevance probabilities.
Illustrative example: Example: Marking one document highly relevant and ordering a collection are distinct output types.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why independently score documents rather than put all of them into one Choice?
Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking
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Answer: Binary prompts estimate whether each document directly answers, making scores sortable across batches after all return. Joint Choice outputs competitive shares whose denominator changes with new documents. Ordinal labeling is separate and retains label order. No BM25 fusion or trained calibration, and zero-relevance queries stay in evaluation.
Illustrative example: Example: Two documents can each be highly likely to answer. In a two-choice distribution they must share probability mass.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does an independent Noul probability differ from adding BM25?
Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking
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Answer: The reranker uses the independent answer probability directly without mixing BM25. Any fusion is a different method and needs separate evaluation.
Illustrative example: Example: BM25 can retrieve the initial set. Adding it again to the final score changes the specified reranker.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Can a missing Portuguese reranker score default to zero?
Jev-as-a-Judge for Brazilian Portuguese Information Retrieval: Relevance Assessment and Reranking
Show answer and example
Answer: No. Collect and match every expected document for the query; reject missing, duplicate or nonfinite entries. Silent zero converts a protocol error into a relevance judgment and changes ranking.
Illustrative example: Example: If the second batch is missing, don't put those documents last and compute metrics anyway.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
How can LLM2Jev use an existing LLM for finite decisions?
LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
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Answer: Read probabilities of option markers to form a distribution without generating an explanation. Fine-tuning adds listwise supervision. This changes readout/optimization, not the need for inference.
Illustrative example: Example: Read numbered-marker probabilities to select a tool instead of generating its full name.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why normalize joint option-string probabilities rather than use the last token?
LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
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Answer: A multi-token option needs its full conditional chain. Fine-tuning adds a listwise objective and a base-model auxiliary KL anchor. Length and tokenization affect readout; the local candidate kernel does not evaluate a full checkpoint.
Illustrative example: Example: For a two-token marker use both conditional probabilities. Taking only the last can rank choices differently.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does the LLM2Jev KL anchor differ from training only a listwise loss?
LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
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Answer: Listwise loss trains current choices; auxiliary KL against the base distribution limits drift on other outputs. It is a stability tradeoff, not a guarantee of retaining all prior abilities.
Illustrative example: Example: Tool selection may improve under the auxiliary constraint, but open-ended tasks still need independent evaluation.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
How should LLM2Jev use the reference for auxiliary KL?
LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
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Answer: Freeze the base distribution and compute KL from base to auxiliary output. Updating the reference with the policy moves the anchor and changes the regularizer.
Illustrative example: Example: As the student changes, the base stays fixed instead of chasing the student.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
How does MetaEncoder use one representation for closed choices and open retrieval?
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Answer: Encode requests and candidates separately with a shared encoder, normalize their final-token embeddings and compare similarity. Closed choice uses supplied options; open retrieval searches a larger bank. Candidate scope and training negatives differ.
Illustrative example: Example: One request vector can choose among three tools or search a full description index.
Sections in the Chinese source: 背景与主要改动, 核心公式与原文效果. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why do cached candidates and options in the request not imply full candidate interaction?
Show answer and example
Answer: Shared dual towers independently encode request and candidate and match normalized last-token vectors. A candidate can be cached because it doesn't depend on the request. Training partly drops closed-set options to discourage surface matching. Cross-reasoning remains limited, and cache requires identical model, prompt and candidate content.
Illustrative example: Example: Pre-encode tools and encode each request online. A changed tool definition invalidates its old vector.
Sections in the Chinese source: 背景与主要改动, 核心公式与原文效果. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
What does MetaEncoder's dual encoder sacrifice relative to joint deep interaction?
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Answer: Separate encoding enables caching and large retrieval, but limits fine-grained pairwise reasoning. Including options in a closed-set request adds context without turning the candidate tower into an arbitrary cross-encoder.
Illustrative example: Example: Detecting a subtle contradiction between long proofs can be harder with fixed vectors than joint reading.
Sections in the Chinese source: 背景与主要改动, 核心公式与原文效果. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Why deduplicate open-retrieval negatives over the full MetaEncoder batch?
Show answer and example
Answer: Open retrieval uses global batch candidates. Treating duplicates as independent negatives changes training mass. The specified direction is request-to-candidate; adding a reverse term changes the objective.
Illustrative example: Example: The same tool appearing twice should not become two competing negatives.
Sections in the Chinese source: 背景与主要改动, 核心公式与原文效果. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
How does MJev-Spatial express a 2D point as classification?
MJev-Spatial: System One Spatial Intelligence via Native Attention Matching
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Answer: Treat image regions as candidates and select progressively finer regions. Quantities such as counts also use buckets or staged readout. The output is typed choice, not free-form coordinate parsing.
Illustrative example: Example: Pick the upper-right region, then one cell in its next 3×3 grid.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why read options using final attention Q/K rather than a separate classifier head?
MJev-Spatial: System One Spatial Intelligence via Native Attention Matching
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Answer: Use the decision marker as query and candidate end markers as keys in the backbone's own projections. This shares a readout for options, numeric buckets and regions. Position encoding and grouped-query mapping matter. Points still need staged selection and crops from the original image; classification alone does not ensure spatial precision.
Illustrative example: Example: Select a coarse region, then crop it from the original image. Repeatedly cropping the last resized image changes coordinates and detail.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does two-stage counting differ from one class per integer?
MJev-Spatial: System One Spatial Intelligence via Native Attention Matching
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Answer: Two smaller candidate sets compose the result as 32a₁+a₂, covering 0–1023. A large head directly scores each integer. Reusing finite-choice readout is useful, but first-stage errors affect the result; matching output spaces do not imply equivalent training.
Illustrative example: Example: a₁=2 and a₂=3 represent 67, not the sum of two independent counts.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Why average multistage spatial losses within each original question first?
MJev-Spatial: System One Spatial Intelligence via Native Attention Matching
Show answer and example
Answer: Otherwise questions with more refinement stages receive more weight. Average stage cross-entropies per question, then average questions.
Illustrative example: Example: Three localization choices should not automatically count three times as much as one choice.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
Why aren't PathJev typed decisions open-ended pathology QA?
PathJev: Decision-Native Vision–Language Modelling for Histopathology
Show answer and example
Answer: Expert labels become structured choices or scores normalized over runtime candidates. This helps evaluation but does not cover all open-ended pathology explanations.
Illustrative example: Example: Choosing a predefined grade is different from writing a diagnostic report.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why isn't PathJev just softmax on pathology images?
PathJev: Decision-Native Vision–Language Modelling for Histopathology
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Answer: Its head combines candidate-linear terms, question interactions and set attention, inherited from earlier work. New evidence depends on pathology-state data, typed expert labels, overlap audit and unseen organs. A small classifier does not prove pathology transfer. Full training was not reproduced; targets stay out of prompts.
Illustrative example: Example: Tumor grades are text candidates scored at runtime and trained with source labels. A softmax gradient alone doesn't validate cross-organ recognition.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does PathJev's unseen-organ test differ from random holdout of seen organs?
PathJev: Decision-Native Vision–Language Modelling for Histopathology
Show answer and example
Answer: Unseen organs test cross-organ transfer. Random holdout mostly tests new samples from familiar distributions. Audit overlap to keep related states out of training. The same inherited head does not make the splits equally hard or equivalent.
Illustrative example: Example: Unseen slices of organ A after training on A are not transfer to organ B.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Can PathJev replace the Score target with an ordinal-distance loss?
PathJev: Decision-Native Vision–Language Modelling for Histopathology
Show answer and example
Answer: Not within the same protocol. The paper uses one-hot grade supervision; distances define a new objective even for ordered labels. Targets never enter prompts, and full training was too costly and not reproduced.
Illustrative example: Example: Grades two and three are adjacent, but changing their penalty is not reproducing the original objective.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Basics
How can one System One multimodal readout produce a full ranking?
Multimodal Reranking Is a System One Decision
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Answer: Encode query and first-stage candidates in one context and read their label probabilities at the first answer position, then sort. The unprocessed tail stays in retrieval order. No ranking text is generated, but inputs still need encoding.
Illustrative example: Example: Read A/B/C probabilities for top images and sort them, leaving later candidates in their original order.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Core mechanism
Why report both candidate probabilities and full-vocabulary label mass?
Multimodal Reranking Is a System One Decision
Show answer and example
Answer: Within-label normalization always sums to one, even when the model barely supports any legal label. Full-vocabulary mass exposes low support. Labels must be distinct single tokens; the local readout is not a separately trained System One model.
Illustrative example: Example: A can lead among letters even when their combined vocabulary mass is tiny. It may rank first without high absolute confidence.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Method comparison
How does setwise readout differ from per-document yes/no scoring?
Multimodal Reranking Is a System One Decision
Show answer and example
Answer: Setwise options compete in one normalized context; binary scores judge relevance independently. Their denominators and interactions differ, so probabilities are not interchangeable. The paper's yes/no arm was not reproduced locally.
Illustrative example: Example: Two relevant documents can each have high yes probability, while two choices share total mass.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Structured Decisions / Typed Readout: From Generated Sentences to Candidate Distributions
Implementation details
Why must candidate labels be distinct single tokens under the current tokenizer?
Multimodal Reranking Is a System One Decision
Show answer and example
Answer: Readout uses one answer-position logit vector. Multi-token labels need conditional continuation scores, and another model's token IDs can name different tokens. Verify each label; do not silently use only the last piece.
Illustrative example: Example: If a new tokenizer splits a letter marker, reject it or define a different protocol rather than reuse old IDs.
Sections in the Chinese source: 背景与主要改动, 核心公式. The example illustrates the mechanism.
Topic guide and reading order
Start with this question¶
When the task is just to select from candidates, how can a model return a usable decision directly?
Takeaway¶
Specify state, question, and candidates before choosing a readout. LLM-as-Jev reads model scores for options into a finite distribution and studies frozen models and KL-anchored fine-tuning. MetaEncoder independently encodes request and candidates, using similarity for closed-set decisions or open retrieval. The former can consider options jointly; the latter can cache candidate representations. Their interaction power and reuse costs differ.
Example¶
With only Search, Calculate, and Abstain as routing actions, returning probabilities is easier to constrain than parsing free text. For millions of candidates, a dual tower can pre-encode tools or items. But a single independent similarity may not express reasoning that compares two candidates in detail.
Common pitfall¶
A valid schema guarantees neither correctness, calibration, independence, nor stable performance across candidate counts. Cached candidate embeddings must not depend on the current request. The local LLM-as-Jev evidence is mechanism-only; MetaEncoder's expensive training and evaluation were not reproduced.
Reading order¶
Start with LLM-as-Jev's finite readout to see why it returns a distribution rather than a sentence. Next compare joint option interactions with MetaEncoder's shared dual tower and independent cached candidates. Check each write-up for local capability status.
- LLM2Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them · Original paper
- MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface · Original paper
Check your understanding¶
Does caching independent candidate encodings eliminate the need to re-encode requests?
Show suggested answer
No. Candidates are reusable, but a new state, question, or image still requires a new request encoding and matching or retrieval. Request-specific information in candidate encodings also invalidates the cache; draw the reuse boundary from actual dependencies.