DeepBrainz-R studies compact systems that preserve state, plan, use tools, verify intermediate work, coordinate specialized agents, and continue across software and knowledge tasks.
Plan → act → verify
Core loop
Long horizon
Failure object
Traces
Evidence mode
Research map
This page stays readable by turning the agenda into program cards and evidence relationships rather than a longer list of themes.
Program 01
Problem
Work fails when objectives and state stop matching the task.
Question
How should systems preserve state across long-running work?
Failure modes
objective drift · context decay
Evidence
task traces · resumption evals
Hypothesis
state checks reduce drift
Program 02
Problem
Code output is easier than repository-scale engineering.
Question
How can software agents plan and verify multi-file work?
Failure modes
patch drift · test misread
Evidence
diffs · CI records
Hypothesis
review loops improve useful patches
Program 03
Problem
Specialized workers can amplify shared mistakes.
Question
How should delegated work be verified and handed off?
Failure modes
handoff loss · coordination failure
Evidence
delegation traces · reviewer disagreement
Hypothesis
verifier roles reduce hidden uncertainty
Program 04
Problem
More work can also mean more inference, memory, and tool cost.
Question
What improves useful work per unit of compute?
Failure modes
tool churn · over-retrieval
Evidence
cost records · ablation notes
Hypothesis
planning and memory can reduce restarts
Evidence chain
Research question
Reasoning
Evaluation
multi-step task quality
Trace
reasoning and revision trail
Artifact
technical note
Research question
Memory
Evaluation
retrieval and resumption
Trace
state record
Artifact
evaluation report
Research question
Software engineering
Evaluation
patch-review-test loop
Trace
issue → diff → CI
Artifact
release note
Research question
Coordination
Evaluation
handoff accuracy
Trace
delegation record
Artifact
failure analysis
Research direction
A system that works once can still fail over time. Long-horizon research asks whether objectives remain stable, memory stays useful, tool actions are checked, delegated work is verified, and useful work improves per unit of compute.
State
Goals, constraints, assumptions, and intermediate conclusions decay when tasks exceed a single context window.
Execution
Objective drift, tool failure, interruption, unverified steps, and compounding errors appear only over longer runs.
Evidence
A claim should point to task traces, action logs, tests, review records, and clear limits.
Reasoning
Traces
Long-horizon reasoning maps to multi-step task traces and failure notes.
Memory
Resume
Memory claims map to retrieval, state preservation, and resumption evaluations.
Software
Patch
Engineering claims map to issue, plan, patch, test, CI, review, and rollback records.
Research questions
Each question ties a desired behavior to the failure mode that makes it difficult and the evidence needed to judge progress.
Public surface
DeepBrainz Labs
Product, research, and evidence paths stay easy to choose without turning the page into an architecture map.
01
State must remain useful without flooding context windows or carrying stale assumptions forward.
02
Repository-scale work needs architecture understanding, multi-file planning, tests, CI interpretation, reviewable patches, and rollback boundaries.
03
Planner, executor, reviewer, and verifier roles need shared memory, clear handoffs, disagreement records, and checks on delegated work.
04
Efficiency should be measured against progress after reasoning, retrieval, tool calls, verification, recovery, and coordination cost.
Agentic systems stack
Reason, remember, plan, act, verify, coordinate, continue, and optimize are not slogans. They are points where long-running systems fail and where evidence should be collected.
Reason
Check whether objectives and constraints survive many dependent steps.
Remember
Retain useful state without stale recall, irrelevant accumulation, or memory pollution.
Act
Inspect tool calls, modified artifacts, failed steps, retries, and recovery paths.
Coordinate
Study planner, executor, reviewer, and verifier handoffs without hiding uncertainty.
Research journey
The site should let technical readers see what is being tested, why it is difficult, how it fails, and what artifact would make progress credible.
Question
For example: how can systems resume work without losing objectives?
Failure
Objective drift, context decay, tool misuse, verification failure, or coordination failure.
System
Memory, planning, training signal, inference control, or review design.
Evidence
Traces, logs, patches, tests, reviews, limits, and release notes.
Long-horizon autonomy
DeepBrainz-R studies preservation of objective, context, memory, intermediate artifacts, tool outcomes, and recovery state across many steps.
Objective drift and context decay.
Memory overload and memory pollution.
Tool failure and tool misuse.
Interruption recovery and resumption.
Software engineering intelligence
Software tasks leave inspectable artifacts: issues, plans, diffs, tests, CI logs, review comments, rollback boundaries, and handoff notes.
Repository-scale reasoning.
Multi-file planning.
Test-driven repair and CI feedback.
Reviewable patches and evidence trails.
Evidence mapping
Reasoning maps to multi-step traces. Memory maps to retrieval and resumption checks. Tool use maps to action logs and verification outcomes. Multi-agent coordination maps to delegation traces and reviewer disagreement notes.
Long-horizon reasoning → task traces.
Memory → retrieval and resumption evaluations.
Tool use → action logs and verification outcomes.
Multi-agent systems → delegation and coordination traces.
Explore next
DeepBrainz-R carries the research questions. DeepBrainz-R1 is the model and release family inside that broader initiative.
DeepBrainz-R
The flagship research initiative and concrete questions.
Open this pathDeepBrainz-R1
The model and release family inside DeepBrainz-R.
Open this pathAgentFoundry Research
The software-work evidence and execution layer.
Open this pathAbout
Mission, thesis, principles, and scientific approach.
Open this pathNext step
The DeepBrainz-R agenda is credible when long-horizon behavior, memory, tool use, software engineering, multi-agent coordination, and efficiency all connect to failure modes and evidence.
