We study whether a variational language model can support a minimal and measurable form of agentic control grounded in its own internal evidence. Our model combines local variational hidden computation (EVE), a homeostatic latent regulator, structurally aware checkpoint retention and a calibrated uncertainty-aware controller operating on top of the retained model. Rather than treating uncertainty as a passive diagnostic measured after prediction, we treat it as an operational signal that can regulate training, support checkpoint retention and guide inference-time intervention. The resulting framework is deliberately focused. It studies a closed-loop form of internal control in which structural and predictive signals become actionable. Empirically, the variational backbone improves over a matched deterministic reference on the language-modeling task while also exhibiting a richer and more usable uncertainty profile. On top of this backbone, the calibrated controller remains active, uses multiple actions under a full agentic evaluation and yields a positive quality-cost trade-off. These results support a precise claim: internal uncertainty can serve not only as a descriptive property of a variational language model, but also as a practical control interface for regulation, checkpoint retention and minimal agentic routing.
Figures & tables
Figure 1: A minimal agentic loop. Internal evidence is observed, mapped to an action, and followed by stronger intervention when required. In the full method, this loop is embedded in a broader pipeline that also includes structurally aware checkpoint retention.
Figure 2: A building metaphor for underused and recruited depth. Before control, the model learns but deeper floors remain dark. After closed-loop intervention, a deeper floor becomes active.
Figure 3: A thermostat metaphor for homeostatic latent regulation. The autopilot keeps the latent regime in a healthy zone: active, stable, and controlled.
Figure 4: Before/after view of the observed system behavior. The variational backbone supports prediction and exposes internal uncertainty signals that are later used by the calibrated controller.
Model
Loss
CE
PPL
Acc.
KL
Local
EVE
5.5340
5.4341
229.09
0.1632
282.2273
0.1309
DET
5.6035
5.6035
271.36
0.1560
0.0000
0.0000
Table 1: Final validation comparison between EVE and DET on the new run.
Figure 5: Observed backbone differences between EVE and DET on selected validation metrics. Positive bars indicate an improvement of EVE relative to DET .
Seed
Best epoch
Val CE
Val PPL
Val Acc.
Val KL
Val Local
EVE S101
3
5.4241
226.82
0.1657
255.1250
0.1338
EVE S202
3
5.4161
225.00
0.1654
304.1061
0.1404
Table 2: Final EVE seed comparison on the new run.
Figure 6: Observed training and validation cross-entropy trajectories for the retained run.
Candidate
λband,high
CE
Local
Frac. High
μ2
Stage1 01
2.00
5.4111
0.1318
0.0856
0.0497
Stage1 02
2.05
5.4094
0.1264
0.0821
0.0484
Stage1 03
2.10
5.4381
0.1267
0.0855
0.0498
Table 3: Stage-1 EVE search summary on the new run.
Model
NLL
ECE
MI
Epi.
Flip
CVaR-NLL
Frac. High
μ2
EVE
5.6404
0.0326
0.1777
0.0325
0.3197
12.5035
0.0826
0.0487
DET
5.6698
0.0792
0.0000
0.0000
0.0000
13.0499
0.0000
0.7370
Table 4: Extended uncertainty and diversity summary on the new run.
Figure 7: Epoch-level latent-state diagnostics for the retained EVE checkpoint family. The figure tracks local activity, the fraction of high-activity units, and latent energy across epochs.
Metric
Value
Selected seed
101
Number of examples
200
Accepted examples
180
Abstained examples
20
Coverage
0.9000
Accepted accuracy
0.1778
Table 5: Full agentic evaluation summary on the new run.
Figure 8: Observed behavior of the calibrated controller in the full multi-action evaluation. The summary box reports coverage, accepted/total examples, and avoided errors relative to direct prediction.
Criterion
Passed
Value
Coverage is sufficient
True
0.9000
Retrieve is used
True
0.3200
Deliberate is used
True
0.2650
Not abstain-dominated
True
0.1000
Utility is positive
True
0.1048
Avoids some direct errors
True
0.0994
Table 6: Agentic verification results on the new run.