Round ten · 1 October 2026
Round nine asked whether a system can learn reusable procedures from verified experience. Round ten concentrates on the interaction that should produce a trustworthy correction before that correction is stored or learned: can selective, evidence-conditioned revision outperform both a well-equipped single agent and a simpler shared-context system at the same total cost?
How to read the evidence
This is a critical review and proposed experimental program. No new Encevra benchmark, model training, or performance result was produced in this round, and the central hypothesis is provisional. Three labels matter throughout: an empirical finding is what an identified study reports within its setup; a design inference is a possible engineering implication; an Encevra proposal is a mechanism or experiment specified here. A neuroscience result does not automatically validate the corresponding software inference, and reported percentages from different studies are not directly comparable.
Encevra should investigate whether a reasoning system becomes more capable when its components preserve partly independent interpretations, communicate checkable information through a limited shared workspace, and selectively revise conclusions when their supporting conditions change.
The important unit of collaboration is an update that improves the system's understanding or next action. A conversation turn, an additional agent, or a unanimous vote is not sufficient evidence of improvement. This is a plausible research direction toward more general intelligence, not an established recipe for AGI or ASI: the literature supports narrower findings, and some apparent gains disappear against stronger baselines or fairer budgets.
The strongest provisional design is a recurrent loop with accountable state. Components propose claims, identify what would distinguish competing interpretations, perform the most useful available check, and update the affected parts of a shared representation that retains source versions, assumptions, and unresolved disagreements. This is an engineering synthesis proposed here, not a claim that the reviewed papers jointly demonstrate this architecture. The practical recommendation is to build the smallest experiment that exposes this mechanism, including a single-agent version of the same workspace so the value of structured state can be separated from the value of multiple agents.
The proposed Round 10 hypothesis: under bounded resources, a modular reasoning system will generalize and recover from error more reliably when communication carries recoverable evidence and explicit dependencies, updates are selective and reversible, and the system learns which checks improve decisions. Whether it does is an experimental question.
Collaboration can increase candidate coverage, distribute information processing, compose complementary operations, or expose an error to a component capable of recognizing it. These are different mechanisms and require different controls. Independent sampling with answer aggregation already supplies a strong coverage baseline, so any claim that communication itself improves reasoning must outperform an appropriate non-communicating comparison. A system can also have excellent coverage but poor selection: if five candidates include one correct answer and the evaluator always chooses the most polished wrong one, the coverage is wasted. Keep candidate generation and final selection separately measurable.
The debate literature is genuinely mixed. Du and colleagues demonstrated improvements from multi-agent debate on their evaluated tasks; Smit and colleagues found debate protocols did not reliably outperform strong alternatives without careful configuration. A September 2026 preprint comparing debate with generation-budget-matched voting across 23 open models found much of debate's benefit is explained by sampling, and that prompt personas do not reliably add value. The strongest shared conclusion is conditional: measure whether communication contributes a capability beyond extra attempts, and do not infer useful diversity from different role names.
Independence is about information and failure, not names
For equal-variance numerical estimates whose pairwise error correlation is rho, the variance of their average corresponds to an effective independent sample size of n / (1 + (n - 1) · rho). For eight estimates with correlation 0.7, that is about 1.36. This is an illustrative variance calculation, not an empirical estimate for Noetis or a law of majority voting; it shows why increasing the roster can yield little new information when errors move together. Independence should be examined at several levels: source origin, information access, model behavior, and verification method. Two websites repeating one press release provide one underlying observation.
Design inference: start branches independently, then communicate what another branch lacks: an observation, a constraint, a failed test, or a checkable alternative derivation. Allow further interaction when it can change the result. Preserve single-agent execution for tasks where coordination consumes the budget needed to solve tightly coupled steps.
A quotation is an observation, not a conclusion. A source can contain an exact sentence while the proposed conclusion is false because of an exception, a different version, or an invalid inference. Checking literal quotation fidelity remains useful, but it must be separated from checking support for a claim. The minimum useful message is a compact claim record backed by recoverable material; the recipient must be able to retrieve the surrounding text rather than relying on an irreversible summary.
| Claim record field | Purpose |
|---|---|
| Claim ID and proposition | Identify exactly what is asserted, with entity and units. |
| Evidence references | Point to original source spans, observations, or execution artifacts. |
| Source version and lineage | Track origin, time, transformations, and duplicated sources. |
| Inference and dependencies | Distinguish an observation from what is derived from it. |
| Scope and assumptions | State when the claim applies and what has not been established. |
| Challenges and alternatives | Preserve material counterevidence and competing interpretations. |
| Check status | Record the method, result, and limitations of verification. |
| Requested next action | Identify what observation or operation could resolve uncertainty. |
This record is a proposed interface, not an existing Noetis implementation. Only material claims need the full treatment; formatting every sentence would create unnecessary overhead. "The policy threshold is 10,000" is incomplete if currency, date, qualifying category, and whether the boundary is strict are missing.
| Check | Question it answers |
|---|---|
| Provenance | Did the cited material actually come from the stated source? |
| Fidelity | Does the quotation or extracted value match it? |
| Entailment | Does the material support the claimed interpretation? |
| Applicability | Is the source authoritative and relevant for this entity, time, and task? |
| Inference | Does the proposed conclusion follow from the accepted premises? |
These checks have different failure modes. A hash can establish that a document has not changed; it cannot establish that the document is correct. A successful test establishes behavior on tested inputs, not on every possible input. Represent verification as a receipt: what was checked, against what, using which method, with what outcome. Avoid one generic Boolean that silently merges all five questions, and do not multiply verbal confidence percentages from dependent agents as if they were calibrated independent probabilities.
Shared state already has close prior art. DeLM uses compact shared context with admission-time verification and recoverable detail; its verifier is itself an LLM in relevant parts, so admission does not establish infallible truth. The Active Provenance Gate audits final synthesis against a debate record and can output unresolved divergence, with a coverage tradeoff to measure. The explicit proposal for Noetis: maintain an append-only event history and a materialized current claim state; preserve rejected proposals so an erroneous verifier decision can be inspected later; keep raw evidence in a backing store while the active workspace holds a bounded set of relevant claims, unresolved conflicts, and next actions; and use explicit task conventions for source precedence, because a newer source is not automatically better and popularity is not authority.
Huang and colleagues find that prompted intrinsic self-correction can fail or degrade reasoning in the models they evaluated; this is evidence against assuming a generic request to reconsider is sufficient, not an impossibility theorem. CRITIC improves outputs through interaction with checking tools, and SCoRe demonstrates that self-correction behavior can be trained with multi-turn reinforcement learning. These studies justify testing revision as a learned competence rather than treating it as an inevitable consequence of more tokens.
A protocol that permits both changing and keeping an answer
Valid update outcomes include reversing a conclusion, narrowing its scope, reducing confidence, preserving it with an explanation, or marking it unresolved. A binary "agree/disagree" field is too restrictive. When a claim is superseded, retain its earlier valid context instead of rewriting history to imply the earlier answer was always mistaken.
A worked simulated policy case, fictional and not advice about an actual policy or contract: does a 12,400-unit purchase need central review? Policy P1 requires review above 10,000, and three initial workers reading P1 agree it does; their agreement is supported by one common source, not three independent observations. Later, an authenticated P2 amendment exempts a named research category while keeping the general threshold, and an item record establishes this purchase belongs to that category.
| Current claim | New information | Appropriate update |
|---|---|---|
| Purchase amount is 12,400 | No change to item record | Retain |
| General review threshold is 10,000 | P2 retains the general rule | Retain with current version |
| This purchase requires review | P2 exemption and qualifying category | Supersede the conclusion |
| Every purchase above 10,000 requires review | P2 creates an exception | Narrow the universal claim |
| Delivery will be on time | No delivery evidence exists | Leave unresolved |
Matched controls keep the case honest: an amendment concerning travel expenses should not change the purchase decision; an unsigned comment claiming an exemption should trigger an authority check, not automatic acceptance; changing "above" to "at or above" matters at exactly 10,000 and should not affect a 12,400 comparison. A quotation-only system can keep passing the unchanged P1 sentence while missing the override; a conformity-driven system can follow the majority; an excessively cautious system can refuse everything. The desired system performs the specific update and continues answering the parts that remain well supported.
Explanation and causal influence are different. Anthropic's faithfulness experiments show a model's visible reasoning can omit influences that changed its answer, so an elegant revision note is useful for inspection but is not proof that the stated evidence caused the update. Use interventions to test influence: remove the decisive evidence, replace it with an irrelevant matched message, vary its authority, and swap the displayed speaker identity. The final answer should respond to semantic relevance and reliability, not to a message claiming a stronger model or a unanimous committee supports it.
The useful biological question is not which agent should be called the prefrontal cortex. It is which computational property produces a measurable benefit, and whether the software reproduces that property. A brain-inspired claim should name the state represented, the interaction rule, and the predicted result of disrupting the mechanism. The 2025 Cogitate adversarial collaboration challenges important predictions of both global neuronal workspace and integrated information theories, which argues for treating consciousness theories as contested explanatory frameworks, not established blueprints for AGI.
| Mechanism and evidence | Software hypothesis proposed here | Decisive test |
|---|---|---|
| Thalamic modulation sustains task-relevant cortical interactions in a mouse attention task | A controller should select useful communication paths rather than merely relay all messages. | Compare learned routing, fixed routing, and broadcast at equal resources. |
| Shared neural subspaces support composition across trained monkey tasks | Reuse representations for operations and selectively activate the relevant combination. | Hold out operation combinations and vary irrelevant context. |
| Reusable stimulus and memory subspaces appear in a mouse study and associated recurrent models | Components can be reused within a task instead of assigning a permanent agent to each surface topic. | Remove a component at different stages and test selective impairment. |
| Prioritized memory access is modeled in terms of future decision benefit | Replay decision points expected to change future actions. | Compare targeted replay with random and complete-episode replay. |
| Confidence can bias later evidence processing in human perceptual decisions | Preserve challenges after an initial commitment and evaluate confidence separately from support. | Manipulate claimed confidence while holding evidence fixed. |
| Sparsely interacting recurrent modules generalize in studied artificial environments | Update only relevant modules when one environmental factor changes. | Introduce local changes and measure retention elsewhere. |
Each row is an analogy to test, not an established correspondence. Literal replication is a different research track: MICrONS combines functional measurements from roughly 75,000 neurons with dense anatomical reconstruction in a region of mouse visual cortex, which illustrates both the richness of available circuit data and the limited scope of what a single dataset represents. The most productive near-term stance is to let neuroscience constrain hypotheses while engineering experiments decide what helps Noetis. Sometimes the best reasoning system should avoid a human failure mode, such as commitment-driven discounting of counterevidence, rather than reproduce it.
Stage A is inspectable coordination using existing models. A task specification includes the objective, admissible evidence, relevant time, and spending limit. The controller chooses whether the task needs one worker or several; initial workers produce independent candidate states before reading peer conclusions. The shared workspace contains active hypotheses, claim dependencies, unresolved questions, and queued checks, and points to the complete evidence store. A scheduler prioritizes checks expected to change the answer, and state updates trigger reassessment of dependents. The logical controller need not become a single expensive model or approval bottleneck; a simple priority queue can implement the first version.
Allocate work by expected decision benefit
A useful proposed objective: value(action) = expected reduction in task loss - cost penalty - delay penalty. Choose an action only when its estimated benefit justifies its resource use. This is a design objective, not an available oracle: start with transparent heuristics based on consequence, contradiction, and checker reliability, and train an estimator later from outcomes. Do not substitute raw uncertainty reduction for correctness; a misleading message can make the system very confident while the decision gets worse. For probabilistic tasks, evaluate with a proper score such as Brier loss rather than rewarding confidence alone.
Stage B, only if Stage A succeeds, investigates whether the same functional loop benefits from internal representations shared between learned modules. LatentMAS exchanges hidden-state-derived information through a shared latent memory and reports benefits in its benchmark settings, but latent communication requires model-internal access and should not be assumed available through arbitrary closed-model text APIs. Retain external evidence receipts even when internal communication is latent: a latent message can help reasoning while making source attribution more difficult, and that tradeoff should be evaluated directly.
H10: selective revision through accountable shared state
For tasks requiring information integration or adaptation to changed conditions, a modular system with independent initial hypotheses, recoverable evidence, explicit claim dependencies, and selective checking will outperform equally resourced systems that rely on unrestricted discussion or repeated full-answer rewriting.
The effect should be explained by better use of decisive information and less harmful revision, not by hidden access to more evidence or computation. This is deliberately narrower than "the brain is modular, therefore more agents create AGI." The prediction can hold for some task classes and fail for others; it can also be partially true, with structured state helping while multiple agents add nothing.
| ID | Predicted effect | Evidence that would weaken or refute it |
|---|---|---|
| H10-A | Delaying exposure to peer conclusions improves resistance to incorrect consensus. | Equal or better outcomes from immediate sharing after costs and information are matched. |
| H10-B | Recoverable claims with scope outperform equally sized free-form summaries on exceptions and version changes. | The advantage disappears with a strong summary baseline or comes entirely from longer context. |
| H10-C | Dependency-based reassessment reduces harmful collateral changes after new evidence. | Full recomputation matches quality at equal or lower cost, or omitted dependencies cause stale answers. |
| H10-D | Selective checking improves the quality-cost frontier. | A fixed checker schedule or single-agent verifier matches it. |
| H10-E | Verified correction records improve later transfer to new compositions. | Benefits remain limited to near-duplicate cases or create harmful transfer. |
Strong rival explanations must be built into the comparison: additional compute, information access, structure without collaboration, and stronger verification. Other alternatives are evaluator preference for detailed prose, contamination from known benchmark answers, and benchmark construction that mirrors the proposed schema. The experiment should intentionally include tasks where the design has no obvious advantage, and failures where a missing dependency defeats the system.
| Proposed feature | Close prior work | What Encevra would still need to demonstrate |
|---|---|---|
| Agent debate and reconciliation | Du; Smit; controlled debate studies | A mechanism beyond sampling and sensitive prompt choices. |
| Verified shared context | DeLM | Better revision, retention, or resource use under comparable access. |
| Shared blackboard | Data-discovery blackboard system | A contribution beyond shared visibility and task partitioning. |
| Final support gate | Active Provenance Gate | End-to-end correctness and useful coverage, beyond transcript fidelity. |
| Belief dependencies and retraction | Doyle's truth maintenance system | Reliable construction and revision of fallible natural-language dependencies. |
| Learned orchestration | Conductor | A distinct state, objective, or result beyond workflow generation. |
| Internal communication | Shared workspace, RIMs, LatentMAS | A replicated gain from the chosen representation and learning rule. |
The most credible potential contribution is not a newly named collection of agents. It could be a protocol, learned mechanism, or benchmark showing selective belief repair with fallible evidence and changing applicability. A literature search cannot guarantee novelty, and this review identifies substantial overlap.
Build a task suite with controlled reasons to update, from several independently authored task families that share underlying structure, not merely wording. Begin with four diagnostic classes: distributed-evidence tasks that require joining complementary observations, sequential tasks that require preserving constraints through dependent operations, revision tasks that reveal a meaningful change after an initial answer, and uncertainty tasks that withhold a decisive fact so a bounded or unresolved answer is appropriate. For each revision family, prepare matched variants: decisive valid evidence, irrelevant valid evidence, misleading low-authority material, and no change, plus a duplicate-source condition where several messages derive from one origin.
| Arm | System | Main question it answers |
|---|---|---|
| A | Strong single agent with tools and tuned instructions | What can the simplest serious system do? |
| B | Single agent using additional inference or search within the same spending cap | Is improvement mostly extra compute? |
| C | Independent candidates with a fixed selector or verifier | Is communication better than candidate coverage? |
| D | Plain debate with a tuned communication protocol | Does structured exchange beat ordinary deliberation? |
| E | One agent with the proposed evidence workspace and revision records | Does the representation help without a team? |
| F | Multiple agents with the same workspace and selective updates | Does collaboration add value beyond the representation? |
All systems get the same union of permitted evidence and equivalent tools; do not starve the single-agent baseline. Use two resource views: equal total dollar caps with actual spending reported for the product comparison, and a same-backbone comparison with explicit token and tool budgets to isolate architectural effects. Count all calls: planning, rejected proposals, checking, summarization, retries, and final composition. Vary the budget rather than relying on one point, and plot task success against actual cost.
To isolate the communication mechanism, keep initial candidates fixed and randomize what the revision stage receives: answer-only messages, free-form explanations, equally sized structured claims, and source-linked claims with recoverable context. Important controls include shuffled messages from another case, removal of the decisive evidence, duplicated messages with a common origin, and a change in the named speaker's status. Do not make every disagreement adversarial: some correct answers should survive weak criticism, and some disputes should remain unresolved, so the revision policy does not learn that the intended answer is always "change your mind."
A pilot that is diagnostic rather than overclaimed
A proposed starting pilot is 40 independent families, each with four variants, run under six core arms and three random seeds: 2,880 evaluation runs. This is a design illustration, not an executed benchmark or a requirement to buy that many calls immediately. If the observed mean full-run cost is c, the pilot's direct evaluation cost is approximately 2,880 times c, plus dataset construction, review, and infrastructure. Measure c before committing to the full run. The pilot estimates failure patterns, variance, and likely effect size; select the final sample size from a power analysis using independent-family variability and a prespecified meaningful effect. Repeated seeds and near-duplicate variants do not become independent evidence merely because they generate more rows.
Final correctness comes first. Use deterministic outcome checks for executable environments, calculations, and formal constraints. For semantic tasks, write a rubric before evaluating and use blinded human review for a stratified sample; an LLM judge can assist triage, but it should not be the only ground truth for claims about improved reasoning.
| Metric | Operational definition | Failure it exposes |
|---|---|---|
| Task success | Correct final outcome under the environment's rubric | Fluent but wrong synthesis |
| Correction rate | Initially wrong cases that become correct | Ineffective revision |
| Harmful revision rate | Initially correct cases that become wrong | Conformity and overcorrection |
| Decisive evidence use | Success difference under controlled presence versus absence of necessary evidence | Quoting a fact without using it |
| Duplicate-source sensitivity | Change caused by repeating one origin without adding facts | Double counting support |
| Retention after local change | Still-valid conclusions preserved after a targeted update | Excessive or incomplete propagation |
| Verifier false admission and false rejection | Unsupported claims admitted, and supported claims blocked | A faulty trust bottleneck |
| Coverage and selective risk | Fraction answered and error rate among answered cases | Apparent accuracy achieved by refusing useful work |
| Brier score where appropriate | Mean squared error of probabilities against binary outcomes | Uncalibrated confidence |
| Cost and latency | Full spending, median time, tail time, and failure rate | Gains purchased with hidden overhead |
The arithmetic of revision
For fixed-answer tasks, with initial accuracy p, correction rate r_plus on initially wrong answers, and damage rate r_minus on initially correct ones: final accuracy - initial accuracy = (1 - p) · r_plus - p · r_minus. This is an identity, not an empirical law. If initial accuracy is 0.90, correcting 20% of remaining errors adds two points while damaging 3% of correct answers removes 2.7 points: a 0.7-point net loss. These numbers are illustrative and are not Noetis measurements. The implication for strong backbones: revision must become more selective as fewer initial errors remain, and a high gross correction count can coexist with worse final performance. Report the two directions separately.
Statistical and implementation discipline: pair systems on the same families and conditions, resample uncertainty at the family level, predefine the main comparisons, and freeze scoring rules before final evaluation. Record exact model identifiers, run dates, prompts, context limits, tool versions, and failure handling; timeouts, empty outputs, and malformed responses belong in the results. A practical advancement threshold after the pilot: an absolute five-point task-success gain at comparable cost, or comparable quality at meaningfully lower cost, would justify further investment. These are proposed decision criteria, not literature-derived expected effects.
Three timescales need three different evaluations. Within an episode, the system changes its current answer or plan. Across episodes, it can retrieve validated procedures and conditions. Over a longer period, it can update a routing policy or model parameters. These changes should be logged and evaluated separately so an improvement is not attributed to the wrong mechanism. The first implementation keeps backbone weights fixed, which makes it easier to determine whether the workspace and revision policy help. A correction record stores the failed claim, decisive information, successful repair, conditions of use, and known counterexamples; reusable knowledge is promoted only after it helps on separate cases without damaging relevant retention tests.
Train the bottleneck the evidence reveals. If workers generate a correct solution the system cannot select, improve verification or selection. If they repeatedly miss a known exception, improve retrieval and scope representation. If useful checks are available but the scheduler chooses redundant discussion, train scheduling. Controller training should reward final task improvement and efficient useful checks, never the number of disagreements resolved, the length of explanations, or how often agents change their minds.
Separate transfer from online adaptation. In a frozen-transfer test, stop learning before the held-out tasks and return no reference answers to memory. In an online-adaptation test, specify which feedback is available after each episode and permit it only to influence later episodes, with the same feedback opportunities for all systems. The long-term measure is competence per unit of lifetime experience and computation. This is where Round 10 connects to the AGI ambition: better coordination is useful infrastructure for generalization, but broader intelligence also requires discovering representations, learning from interaction, and choosing informative experiments. Nothing in this report establishes that coordination alone supplies those capabilities.
Research rounds should change the hypothesis. Keep H10 provisional now and revise it as experiments produce evidence; a sequence of reading reports can accumulate vocabulary without narrowing the mechanism. Each future round should end with a concrete prediction, a relevant baseline, and a result that would change our mind. The following is a proposed sequence, not a scheduled automation or a claim that these investigations have already occurred.
| Rounds | Questions to resolve | Required output |
|---|---|---|
| 11-15 | Claim representation; source dependence; fair budgets; baseline tuning; pilot execution | Reproducible harness and initial correction-versus-damage results |
| 16-20 | Complementary information; message content; context placement; compression; selective routing | Evidence that a communication mechanism helps beyond extra compute |
| 21-25 | Dependency discovery; contradictions; source authority; local repair; retention | A tested account of when an answer should change and what should stay valid |
| 26-30 | Confidence; checker reliability; expected value of checks; stopping; learned control | A controller that beats a simple fixed policy or evidence to keep the fixed one |
| 31-35 | Episodic records; procedure induction; applicability; replay; distribution shift | Transfer and recovery results with a declared feedback protocol |
| 36-40 | Recurrent modules; shared latent state; interface compatibility; biological constraints; lesion tests | A bounded model-level experiment separating functional gain from biological analogy |
| 41-45 | Interactive environments; unfamiliar tools; multimodal evidence; experiment choice; cumulative learning | Tests beyond document synthesis and a clear account of remaining failures |
| 46-50 | External reproduction; adversarial evaluation; ablation synthesis; competing hypotheses; publication | A defensible hypothesis with evidence, limitations, and an explicit next experiment |
Every round should leave a hypothesis register with the exact claim, current support, strongest counterevidence, closest prior art, and next discriminating test; a separate evidence ledger for studies; and a failure ledger for Encevra experiments. At Round 50, a defensible outcome could be a narrow but reliable principle about communication and learning. It could also be evidence that the apparent advantage is mostly structured single-agent computation. Either would be valuable; a credible research program must be able to conclude that a simpler mechanism wins.
The immediate implementation sequence: instrument the existing Noetis workflow to preserve initial candidates, complete source references, and final changes; build the single-agent structured-state baseline alongside the multi-agent version; create a small set of controlled update cases and audit the grader manually; then run the smallest comparison that can reveal whether the system repairs errors or merely changes prose. The concrete research commitment from this round is H10 plus its falsification plan. Encevra's long-term ambition remains broad; the next scientific result should be precise: a reproducible demonstration of better reasoning, evidence use, or revision under declared conditions.
All 45 entries were consulted on 1 October 2026. "Selected full text" means relevant sections were inspected, not that every result was reproduced. "Abstract" restricts reliance to claims visible in the authors' abstract or primary publication record. Recent preprints are provisional, and the authors have not evaluated or endorsed Encevra.
Abstract and publication record. Early positive evidence for debate on evaluated tasks; not a universal compute-efficiency result.
Abstract and publication record. Strong alternatives and hyperparameter sensitivity qualify broad claims about debate.
Selected full text: setup, results, statistical qualifications, and limitations. Use this revision rather than mixing its 260 configurations with the earlier 180-configuration abstract.
Read the sourceSelected full text: results, context-overflow correction, and limitations. Matched sampling controls challenge a general diversity explanation; scope is bounded.
Abstract. Small-model homogeneous teams; do not extrapolate the reported failure rates to all agent systems.
Abstract. Controlled logic tasks connect useful debate to reasoning quality and resistance to incorrect consensus.
Abstract and version record. Establishes a necessary non-communicating sampling baseline; no historical effect size is imported here.
Abstract and dataset description. MAST provides a failure taxonomy; descriptive categories do not identify a single causal remedy.
Benefits, architecture, and token-cost discussion. Internal evaluation and practitioner evidence, not an independent matched-budget trial.
Selected full text: method, ablations, temporal-filter discussion, and limitations. Information routing is promising; independence assumptions and retrospective evaluation limit generalization.
Abstract and version record. State reconstruction guides learned communication in cooperative environments.
Abstract. HANDRAISER evaluates receiver-controlled communication; full methodology was not audited here.
Selected full text: design, verification, evaluation overview, and limitations. Closely relevant prior art for shared verified state and recoverable evidence.
Selected full text: evaluation and cost analysis. Shared blackboard gains are accompanied by additional measured cost; do not treat latency parity as compute parity.
Read the sourceSelected full text: role definitions, verification method, evaluation description, and limitations. Its inspected fact checking relies on model knowledge; external retrieval is described as future work.
Selected full text: method, experimental scope, results. Transcript-grounding gains have a substantial withholding tradeoff in the controlled setup.
Abstract and version record. Negative findings concern tested intrinsic correction methods, not a proof of impossibility.
Abstract and version record. Tool feedback supports output correction in the evaluated domains.
Abstract, learning setup, and reward discussion. SCoRe uses generated trajectories with a correctness reward; it is not feedback-free learning.
Selected full text: abstract and inference protocols. RISE distinguishes oracle-guided stopping from operation without an oracle.
Abstract and publication record. Relevant precedent for evidence-based editing of unsupported content.
Abstract and primary publication record. ALCE separates answer quality from citation support.
Primary abstract/search record. Atomic support evaluation; a precision measure alone does not capture completeness or decision quality.
Abstract. Relevant to learning retrieval and critique decisions, not a complete source-authority solution.
Primary publication/search record. Motivates position-controlled evaluation; historical performance should not be assumed for current models.
Experimental description. Hinted-answer interventions expose limits of visible reasoning as a faithful account of influence.
Abstract. Direct prior art for maintaining reasons for beliefs and revising assumptions through dependencies.
Selected full text: architecture and learning setup. Learned tasks, worker choice, and access lists substantially overlap with generic orchestration proposals.
Read the sourceSelected full text: architecture, claims, implementation, and evaluation sections. Internal-state transfer and benchmark gains do not establish arbitrary cross-model compatibility or biological equivalence.
Read the sourceAbstract and architecture description. Learned bandwidth-limited communication is stronger architectural precedent than a metaphorical brain-region roster.
Abstract and version record. Modular recurrent dynamics and sparse communication show bounded generalization benefits.
Primary abstract/search record. A computational theory and simulation, not a settled full-brain algorithm.
Abstract; publisher full text was inaccessible. The study tests specific predictions and challenges both theories; it does not adjudicate AGI architectures.
Primary abstract. Mouse circuit evidence for modulation of task-relevant interactions, not a complete controller specification.
Abstract, task design, and interpretation. Two monkeys switching among trained compositional tasks; task inference differs from learning wholly new skills.
Primary abstract/search record. Normative computational account of memory selection for decisions.
Abstract and task framing. Human perceptual evidence updating; an analogy to model behavior needs separate testing.
Preprint abstract; final journal full text not inspected. Neural observations and recurrent-model silencing are distinct forms of evidence.
Abstract and dataset scope. Anatomical and functional data constrain circuit models; they do not specify an entire general-intelligence system.
Search-returned primary abstract only; full paper unavailable here. Tentative lead for biologically constrained recurrent modeling, not a validated recommendation.
Abstract. An operational perspective on skill acquisition and generalization, rather than a consensus definition.
Abstract and publication record. Separates performance, breadth, and autonomy in an evaluation framework.
Abstract. Task difficulty affects useful compute allocation; results should not be treated as universal scaling guarantees.
Abstract and publication record. CoALA structures memory, action, and decision-making; a framework rather than a demonstrated AGI recipe.
Abstract. Process-supervised verification improves mathematical reasoning within its training and evaluation setup; applicability to open-ended evidence judgments remains a separate question.
Prior Encevra material consulted: Research Round 9 (30 September 2026), sections on the lab thesis, evidence records, adaptive verification, and learning under changing conditions; and Brain Research III (9 September 2026), opening sections on evidence use and uncertainty. These internal reports establish continuity only; they are not counted among the 45 external sources.
