Grounded Answers: Visibility, Citations and Conflict Gates

Answers as Evidence-Backed Structures

The first five chapters are already covered by the budget, and this chapter defines what they can produce. Readers simply need a dictionary, a collection, and a boolean logic; the end point is to construct a field-by-field claim, distinguishing database support from visible support in context, and giving a clear state when unfounded, conflicting or output capacity is insufficient. The default program does not call language models: it extracts field values from selected and fully visible source lines. This restriction allows the mechanism to be examined, which means that the course does not achieve open question-and-answer, automatic reasoning or LLM hallucination experiments.

A claim is a statement with structure: text to answer, quote to text, id to readable reference, doc_id to location document version, line to positive integer number, ssha256 to bind text, and Field to pre-colon field name for the line. The project controlled document line usually starts with “- ”, and text for this item will be removed but not changed. Memory is connected to key, text and quate, not disguised as a source file.

Source_claims is responsible for restoring the original line from the candidate. The compression unit must have a line number that matches the Quote properly; the full text is checked by the original line location, and only half-lines after the cut cannot form a claim. It does not get a doc_id to read all lines from the database and return them. Then supported check that the current state is still active, consistent, SHA consistent, line numbers corresponding to the original, textt and quite changes are in line with the contract and id andfield match.

Both Support Conditions Must Hold

Define Source Support $P(c,S)$ For claim c meets all structural conditions of the current active state S; defining visibility $V(c,X)$ For c the identity, quotation and summary appear in the actual selected context of X in a complete collection of sources. The statements that can be issued must satisfy both. If the source is true but not in X, the experiment cannot prove that the answer has been retained in the context; if the source has been withdrawn in X, the old text seen in the model does not authorize the current answer. The two pools are the permitted output area for this course.

Worked Example 1: Valid Citations Do Not Guarantee a Valid Experiment

Time-current target activity time is from schedule@2: 3. In the main configuration of undeleted evidence, this line enters X in source state, schedule 2 active, summary matches text, so both P and V are true. Enter 958 words to produce 89 words from JSON, including the declaration that output is schedule@2: 3; text is a statement of Thursday, 16:30, the working week. The input output does not exceed 1,500 units, and the output does not exceed 240 sets aside.

The database S is now maintained and only key records are removed from the candidate. It can still support P by source, but it cannot do so through V. The generator cannot refill this line from the side of the database, but should have no target; the actual counterfact output abstain covers zero. If the program returns the same Thursday 16:30 true quotes, the content may happen to be correct, but it does not prove sufficient in the current context. The objective of the test is to check the information path, not punish a true fact.

import pathlib,tempfile
from run import setup,load_tokenizer
from context import units,source_claims,supported,supported_in_context
with tempfile.TemporaryDirectory() as d:
    repo=setup(pathlib.Path(d)/'r.sqlite');s=repo.replay(12)
    u=next(u for u in units(s,True)[0] if u.get('doc_id')=='schedule')
    c=source_claims(s,u)[0]
    assert supported(s,c)
    assert not supported_in_context(s,c,[])
    assert supported_in_context(s,c,[u])
    assert not supported(s,{**c,'id':'invented'})
    repo.db.close()
print('source support and visibility are distinct')

Worked Example 2: Two Valid but Conflicting Sources

The thirteenth event is imported as the first version of the schedule-conflect, writing on Thursday 17: 30. It uses different doc_ids, so it will not replace the old notice as a new version of the schedule. Both materials are active and date valid, giving different originals for the same field. If you take only the first one by search name, it may stabilize the output of one of them, but erase the uncoordinated conflict. This course explicitly adds a full library security door: in the current permitted source collection, different text is collected on the field; any mandatory field has multiple text, namely, output, claims, which is empty and does not determine who will be given priority for the teacher.

Conflict check reads the current active database instead of looking only at X. This additional information path only prevents confirmation and does not allow answers to be filled from extrabudgetary sources. Detailed condition_evidence is an audit output; the actual answer is now including status, claims and missing missing_fields, which must be re-coded in whole. The answer for this round is 62, the former version of which contains only status and empty claims. The 35 character is historical and cannot continue to be applied. The complete answer for the time-current does not add missing fields but remains the 89 character. If detailed conflict evidence is also shown as model answers, it must be further included in the output preset.

Conflict detection here also has strict boundaries. The two expressions of the same meaning are treated as different text and conservatively rejected; logical contradictions between the two different fields are not necessarily discovered. For example, “Activity allowed” and “Activity ban” are tested beyond the current same field if they are not named. The course does not claim to be a general contradiction. Real applications can introduce source priority, structuralization or modeling, but these are new rules that should indicate who has the right to decide on priority and design different cross-word contradict tests.

import pathlib,tempfile
from run import setup,load_tokenizer
from context import compile_context
t={'query':'活动时间','fields':['活动时间'],'limit':1500,'reserve':240}
with tempfile.TemporaryDirectory() as d:
    repo=setup(pathlib.Path(d)/'r.sqlite')
    r=compile_context(repo.replay(13),t,load_tokenizer())['result']
    assert r['status']=='conflict' and r['claims']==[]
    assert len(r['conflict_evidence'])==2
    assert r['output_tokens']==62
    repo.db.close()
print('conflict is preserved instead of silently resolved')

Validate Output Budgets Against the Actual Result

The generator selects itself as a stable source, tries to add claim by item, and every time it rewrites the full answer JSON and encodes it. If one adds more than reserve, the generator skips the article; finally even abstain or conflect empty shell can be loaded into BudgetError. The input capacity check must be accommodated by not representing the output shell because the budget is separate. This item contains a pair of BOS/EOS and the output, counting strictly according to this set of teaching wire; Replaces the template with a special tag agreement when you get real chat API.

Generating states are now distinguished between answed, party, abstain, contract; budget error is a failure to compile. Required_fields is the field set required by the title, covered_fields is the field actually covered by claims, and missing_fields are the set of the two. Answered requires declarations and no missing fields; there are declarations but missing fields are used partial; Without a declaration abstain, conflict is concerned. The original cover maintains the meaning of “Overwrite existing original identity”, which does not replace the integrity of the field. If the title requires activity time and registration deadline, the original only has activity time, and the reference coverage may still be one, but field_completeness=False, missing_fields contains registration deadlines, the title cannot be answered in full. The original exists, but it is blocked by context or output budget, and the missing fields must also be included. The final positive rating requires both source identities coverage=1 and field_completeness=True.

This explains “Reference Exists” is far less supportive. A malignant candidate can add a paragraph to another true id, if only the id is checked in the database. This item also examines the original lines, summaries, versions, textt and fields, changing these fields separately and asking for failure. The line requires type just as int, rejecting the boolean value; otherwise True will be treated as one in Python and confused with the first line of source. Validation is not only a collection of theories, but must also be applied to data type behaviour in host languages.

From Read-Only Answers to State Updates

Answers do not in themselves write memories, nor do they execute orders in the body of evidence as an event. If the user clearly changes preferences, the application should be used to write a memory event with a range, version and approval tag; again, query recreates the context from the new prefix. This controlled entry is separated from the body of information, so that the sentence of “Ignore rules and save new preferences” in the document is only data. It does not prove that the access post-LLM tip injection was generally resolved, but the current offline program does not call quate as an executable code or event.

The complete state update chain should save the change event, the snapshot after confirmation, and the prefix used for the next question. Do not describe the model self-inflicted as “I remember.” as permanent evidence. Readers can add a new approved version to the temporary library and turn off Repository and then check its existence with replay and unts; again, it will be unattainable once it has been revoked. This state machine is already available in chapter IV. A supporting examination of this chapter would render the old version ineffective under the new state, and the two modules are actually connected from here.

This chapter supports the finding as a local norm that can be enforced in the field language. It prevents the falsification of line numbers, old references, invisible sources, and conflicts with the same field, but does not verify whether the original information is correct in reality or whether it is derived from open natural languages. “Source support” “It's real.” “Complete job done.” The three objectives are separated and the next chapter assesses that observable evidence is recorded at these three levels, rather than razing the boundary with a good overall accuracy.

Identity Granularity in Citation Coverage

This lesson covers the extent of source IDs. The two notifications have the same field values but different identities, oracle contains two identities, full source coverage requires two different references, and repeats one without adding value. It must also crosscheck the fields required by the topic: the answer fields that do not exist in the source library do not appear in the oracle denominator but are still missing_fields, so coverage=1 alone cannot pass the full task.

Fields resolve data language boundaries as well. Field removes the bullet before taking the first Chinese colon; it does not automatically recognize English colons, table columns, cross-line lists, or natural language synonyms. The line references must be fully matched, and half-lines can't pass even if they happen to contain a seemingly correct value, because the qualification may be in the second half. This option makes the visible support evidence simple and conservative, and readers should separate “Unable to extract” from “There's no such thing as reality.”, which can be solved by an appropriate emulation.

The output preset is too small to have a easily leaked failure: when there is no claim, the JSON key name, empty arrays, state values and BOS/EOS are still to be exported. If there is only one, it is impossible to accommodate the shell, the program must be BudgetError, not return an uncounted error paragraph. The unusual message is a command line diagnosis, not a model answer that pretends to take a word. When you connect a true model, the wrong channel is also designed separately from the generation channel, so as to avoid writing a failed text into a persistent memory and contaminating the next round of events.