Human Feedback Types
missingNone explicit
No explicit feedback protocol extracted.
"Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues."
HFEPX · Eval paper review
Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation
Ashish Rana, Chia-Chien Hung, Qumeng Sun, Julian Martin Kunkel +1 more
Published
Mar 31, 2026
Citations
0
Trust level
Low
Usefulness score
15/100 (Low)
Extraction confidence
45% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Mar 31, 2026
Should you rely on this paper?
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The available metadata is too thin to trust this as a primary source.
- The abstract does not clearly name benchmarks or metrics.
Best use
Background context only
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The available metadata is too thin to trust this as a primary source.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
15/100
Adjacent candidate
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Abstract
Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues. In contrast, memory-augmented LLM agents rely on "always-on" retrieval and "flat" memory storage, causing high interference and latency as histories grow. We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion. Oblivion decouples memory control into read and write paths. The read path decides when to consult memory, based on agent uncertainty and memory buffer sufficiency, avoiding redundant always-on access. The write path decides what to strengthen, by reinforcing memories contributing to forming the response. Together, this enables hierarchical memory organization that maintains persistent high-level strategies while dynamically loading details as needed. We evaluate on both static and dynamic long-horizon interaction benchmarks. Results show that Oblivion dynamically adapts memory access and reinforcement, balancing learning and forgetting under shifting contexts, highlighting that memory control is essential for effective LLM-agentic reasoning. The source code is available at https://github.com/nec-research/oblivion.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
partialAutomatic Metrics
Includes extracted eval setup.
"Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues."
Quality Controls
missingNot reported
No explicit QC controls found.
"Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues."
Reported Metrics
missingNot extracted
No metric anchors detected.
"Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- No
- Feedback types
- None
- Rater population
- Not reported
- Expertise required
- Coding
Evaluation details
- Evaluation modes
- Automatic Metrics
- Agentic eval
- Long Horizon
- Quality controls
- Not reported
- Evidence quality
- Low
- Use this page as
- Background context only
Research brief
Metadata summary
Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues.
- In contrast, memory-augmented LLM agents rely on "always-on" retrieval and "flat" memory storage, causing high interference and latency as histories grow.
- We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Validate inferred eval signals (Long-horizon tasks) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues.
- We introduce Oblivion, a memory control framework that casts forgetting as decay-driven reductions in accessibility, not explicit deletion.
- We evaluate on both static and dynamic long-horizon interaction benchmarks.
Why it matters for eval
- Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual cues.
- We evaluate on both static and dynamic long-horizon interaction benchmarks.
Researcher checklist
-
Human feedback protocol is explicit
No explicit human feedback protocol detected.
-
Evaluation mode is explicit
Detected: Automatic Metrics
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.